diff --git a/doc/pub/week44/html/._week44-bs033.html b/doc/pub/week44/html/._week44-bs033.html
index 156dd4309..beb54d4d7 100644
--- a/doc/pub/week44/html/._week44-bs033.html
+++ b/doc/pub/week44/html/._week44-bs033.html
@@ -72,46 +72,48 @@ Automatically generated HTML file from DocOnce source
('Classification tree, how to split nodes', 2, None, '___sec18'),
('Visualizing the Tree, Classification', 2, None, '___sec19'),
('Visualizing the Tree, The Moons', 2, None, '___sec20'),
- ('Algorithms for Setting up Decision Trees', 2, None, '___sec21'),
- ('The CART algorithm for Classification', 2, None, '___sec22'),
- ('The CART algorithm for Regression', 2, None, '___sec23'),
- ('Computing the Gini index', 2, None, '___sec24'),
+ ('Other ways of visualizing the trees', 2, None, '___sec21'),
+ ('Printing out as text', 2, None, '___sec22'),
+ ('Algorithms for Setting up Decision Trees', 2, None, '___sec23'),
+ ('The CART algorithm for Classification', 2, None, '___sec24'),
+ ('The CART algorithm for Regression', 2, None, '___sec25'),
+ ('Computing the Gini index', 2, None, '___sec26'),
('Simple Python Code to read in Data and perform Classification',
2,
None,
- '___sec25'),
- ('Computing the Gini Factor', 2, None, '___sec26'),
- ('Entropy and the ID3 algorithm', 2, None, '___sec27'),
+ '___sec27'),
+ ('Computing the Gini Factor', 2, None, '___sec28'),
+ ('Entropy and the ID3 algorithm', 2, None, '___sec29'),
('Cancer Data again now with Decision Trees and other Methods',
2,
None,
- '___sec28'),
- ('Another example, the moons again', 2, None, '___sec29'),
- ('Playing around with regions', 2, None, '___sec30'),
- ('Regression trees', 2, None, '___sec31'),
- ('Final regressor code', 2, None, '___sec32'),
- ('Pros and cons of trees, pros', 2, None, '___sec33'),
- ('Disadvantages', 2, None, '___sec34'),
+ '___sec30'),
+ ('Another example, the moons again', 2, None, '___sec31'),
+ ('Playing around with regions', 2, None, '___sec32'),
+ ('Regression trees', 2, None, '___sec33'),
+ ('Final regressor code', 2, None, '___sec34'),
+ ('Pros and cons of trees, pros', 2, None, '___sec35'),
+ ('Disadvantages', 2, None, '___sec36'),
('Ensemble Methods: From a Single Tree to Many Trees and Extreme '
'Boosting, Meet the Jungle of Methods',
2,
None,
- '___sec35'),
- ('An Overview of Ensemble Methods', 2, None, '___sec36'),
- ('Bagging', 2, None, '___sec37'),
- ('More bagging', 2, None, '___sec38'),
- ('Simple Voting Example, head or tail', 2, None, '___sec39'),
- ('Using the Voting Classifier', 2, None, '___sec40'),
+ '___sec37'),
+ ('An Overview of Ensemble Methods', 2, None, '___sec38'),
+ ('Bagging', 2, None, '___sec39'),
+ ('More bagging', 2, None, '___sec40'),
+ ('Simple Voting Example, head or tail', 2, None, '___sec41'),
+ ('Using the Voting Classifier', 2, None, '___sec42'),
('Please, not the moons again! Voting and Bagging',
2,
None,
- '___sec41'),
- ('Bagging Examples', 2, None, '___sec42'),
+ '___sec43'),
+ ('Bagging Examples', 2, None, '___sec44'),
('Making your own Bootstrap: Changing the Level of the Decision '
'Tree',
2,
None,
- '___sec43')]}
+ '___sec45')]}
end of tocinfo -->
@@ -170,29 +172,31 @@ MathJax.Hub.Config({
from sklearn.tree import DecisionTreeRegressor
+. random. seed(6 )
+Xs = np. random. rand(100 , 2 ) - 0.5
+ys = (Xs[:, 0 ] > 0 ). astype(np. float32) * 2
-tree_reg1 = DecisionTreeRegressor(random_state=42 , max_depth=2 )
-tree_reg2 = DecisionTreeRegressor(random_state=42 , max_depth=3 )
-tree_reg1. fit(X, y)
-tree_reg2. fit(X, y)
+angle = np. pi/4
+rotation_matrix = np. array([[np. cos(angle), - np. sin(angle)], [np. sin(angle), np. cos(angle)]])
+Xsr = Xs. dot(rotation_matrix)
-def plot_regression_predictions (tree_reg, X, y, axes= [0 , 1 , -0.2 , 1 ], ylabel= "$y$" ):
- x1 = np. linspace(axes[0 ], axes[1 ], 500 ). reshape(-1 , 1 )
- y_pred = tree_reg. predict(x1)
- plt. axis(axes)
- plt. xlabel("$x_1$" , fontsize=18 )
- if ylabel:
- plt. ylabel(ylabel, fontsize=18 , rotation=0 )
- plt. plot(X, y, "b." )
- plt. plot(x1, y_pred, "r.-" , linewidth=2 , label= r"$\hat {y} $" )
+tree_clf_s = DecisionTreeClassifier(random_state=42 )
+tree_clf_s. fit(Xs, ys)
+tree_clf_sr = DecisionTreeClassifier(random_state=42 )
+tree_clf_sr. fit(Xsr, ys)
plt. figure(figsize= (11 , 4 ))
plt. subplot(121 )
-plot_regression_predictions(tree_reg1, X, y)
-for split, style in ((0.1973 , "k-" ), (0.0917 , "k--" ), (0.7718 , "k--" )):
- plt. plot([split, split], [-0.2 , 1 ], style, linewidth=2 )
-plt. text(0.21 , 0.65 , "Depth=0" , fontsize=15 )
-plt. text(0.01 , 0.2 , "Depth=1" , fontsize=13 )
-plt. text(0.65 , 0.8 , "Depth=1" , fontsize=13 )
-plt. legend(loc= "upper center" , fontsize=18 )
-plt. title("max_depth=2" , fontsize=14 )
-
+plot_decision_boundary(tree_clf_s, Xs, ys, axes= [-0.7 , 0.7 , -0.7 , 0.7 ], iris= False )
plt. subplot(122 )
-plot_regression_predictions(tree_reg2, X, y, ylabel= None )
-for split, style in ((0.1973 , "k-" ), (0.0917 , "k--" ), (0.7718 , "k--" )):
- plt. plot([split, split], [-0.2 , 1 ], style, linewidth=2 )
-for split in (0.0458 , 0.1298 , 0.2873 , 0.9040 ):
- plt. plot([split, split], [-0.2 , 1 ], "k:" , linewidth=1 )
-plt. text(0.3 , 0.5 , "Depth=2" , fontsize=13 )
-plt. title("max_depth=3" , fontsize=14 )
-
-plt. show()
-
-
-
-
= DecisionTreeRegressor(random_state=42 )
-tree_reg2 = DecisionTreeRegressor(random_state=42 , min_samples_leaf=10 )
-tree_reg1. fit(X, y)
-tree_reg2. fit(X, y)
-
-x1 = np. linspace(0 , 1 , 500 ). reshape(-1 , 1 )
-y_pred1 = tree_reg1. predict(x1)
-y_pred2 = tree_reg2. predict(x1)
-
-plt. figure(figsize= (11 , 4 ))
-
-plt. subplot(121 )
-plt. plot(X, y, "b." )
-plt. plot(x1, y_pred1, "r.-" , linewidth=2 , label= r"$\hat {y} $" )
-plt. axis([0 , 1 , -0.2 , 1.1 ])
-plt. xlabel("$x_1$" , fontsize=18 )
-plt. ylabel("$y$" , fontsize=18 , rotation=0 )
-plt. legend(loc= "upper center" , fontsize=18 )
-plt. title("No restrictions" , fontsize=14 )
-
-plt. subplot(122 )
-plt. plot(X, y, "b." )
-plt. plot(x1, y_pred2, "r.-" , linewidth=2 , label= r"$\hat {y} $" )
-plt. axis([0 , 1 , -0.2 , 1.1 ])
-plt. xlabel("$x_1$" , fontsize=18 )
-plt. title("min_samples_leaf= {} " . format(tree_reg2. min_samples_leaf), fontsize=14 )
+plot_decision_boundary(tree_clf_sr, Xsr, ys, axes= [-0.7 , 0.7 , -0.7 , 0.7 ], iris= False )
plt. show()
. show()
42 43 ...
-
45
+
47 »
diff --git a/doc/pub/week44/html/._week44-bs034.html b/doc/pub/week44/html/._week44-bs034.html
index 63d6f7f32..e6d465a98 100644
--- a/doc/pub/week44/html/._week44-bs034.html
+++ b/doc/pub/week44/html/._week44-bs034.html
@@ -72,46 +72,48 @@ Automatically generated HTML file from DocOnce source
('Classification tree, how to split nodes', 2, None, '___sec18'),
('Visualizing the Tree, Classification', 2, None, '___sec19'),
('Visualizing the Tree, The Moons', 2, None, '___sec20'),
- ('Algorithms for Setting up Decision Trees', 2, None, '___sec21'),
- ('The CART algorithm for Classification', 2, None, '___sec22'),
- ('The CART algorithm for Regression', 2, None, '___sec23'),
- ('Computing the Gini index', 2, None, '___sec24'),
+ ('Other ways of visualizing the trees', 2, None, '___sec21'),
+ ('Printing out as text', 2, None, '___sec22'),
+ ('Algorithms for Setting up Decision Trees', 2, None, '___sec23'),
+ ('The CART algorithm for Classification', 2, None, '___sec24'),
+ ('The CART algorithm for Regression', 2, None, '___sec25'),
+ ('Computing the Gini index', 2, None, '___sec26'),
('Simple Python Code to read in Data and perform Classification',
2,
None,
- '___sec25'),
- ('Computing the Gini Factor', 2, None, '___sec26'),
- ('Entropy and the ID3 algorithm', 2, None, '___sec27'),
+ '___sec27'),
+ ('Computing the Gini Factor', 2, None, '___sec28'),
+ ('Entropy and the ID3 algorithm', 2, None, '___sec29'),
('Cancer Data again now with Decision Trees and other Methods',
2,
None,
- '___sec28'),
- ('Another example, the moons again', 2, None, '___sec29'),
- ('Playing around with regions', 2, None, '___sec30'),
- ('Regression trees', 2, None, '___sec31'),
- ('Final regressor code', 2, None, '___sec32'),
- ('Pros and cons of trees, pros', 2, None, '___sec33'),
- ('Disadvantages', 2, None, '___sec34'),
+ '___sec30'),
+ ('Another example, the moons again', 2, None, '___sec31'),
+ ('Playing around with regions', 2, None, '___sec32'),
+ ('Regression trees', 2, None, '___sec33'),
+ ('Final regressor code', 2, None, '___sec34'),
+ ('Pros and cons of trees, pros', 2, None, '___sec35'),
+ ('Disadvantages', 2, None, '___sec36'),
('Ensemble Methods: From a Single Tree to Many Trees and Extreme '
'Boosting, Meet the Jungle of Methods',
2,
None,
- '___sec35'),
- ('An Overview of Ensemble Methods', 2, None, '___sec36'),
- ('Bagging', 2, None, '___sec37'),
- ('More bagging', 2, None, '___sec38'),
- ('Simple Voting Example, head or tail', 2, None, '___sec39'),
- ('Using the Voting Classifier', 2, None, '___sec40'),
+ '___sec37'),
+ ('An Overview of Ensemble Methods', 2, None, '___sec38'),
+ ('Bagging', 2, None, '___sec39'),
+ ('More bagging', 2, None, '___sec40'),
+ ('Simple Voting Example, head or tail', 2, None, '___sec41'),
+ ('Using the Voting Classifier', 2, None, '___sec42'),
('Please, not the moons again! Voting and Bagging',
2,
None,
- '___sec41'),
- ('Bagging Examples', 2, None, '___sec42'),
+ '___sec43'),
+ ('Bagging Examples', 2, None, '___sec44'),
('Making your own Bootstrap: Changing the Level of the Decision '
'Tree',
2,
None,
- '___sec43')]}
+ '___sec45')]}
end of tocinfo -->
@@ -170,29 +172,31 @@ MathJax.Hub.Config({
Classification tree, how to split nodes Visualizing the Tree, Classification Visualizing the Tree, The Moons
-
Algorithms for Setting up Decision Trees
-
The CART algorithm for Classification
-
The CART algorithm for Regression
-
Computing the Gini index
-
Simple Python Code to read in Data and perform Classification
-
Computing the Gini Factor
-
Entropy and the ID3 algorithm
-
Cancer Data again now with Decision Trees and other Methods
-
Another example, the moons again
-
Playing around with regions
-
Regression trees
-
Final regressor code
-
Pros and cons of trees, pros
-
Disadvantages
-
Ensemble Methods: From a Single Tree to Many Trees and Extreme Boosting, Meet the Jungle of Methods
-
An Overview of Ensemble Methods
-
Bagging
-
More bagging
-
Simple Voting Example, head or tail
-
Using the Voting Classifier
-
Please, not the moons again! Voting and Bagging
-
Bagging Examples
-
Making your own Bootstrap: Changing the Level of the Decision Tree
+
Other ways of visualizing the trees
+
Printing out as text
+
Algorithms for Setting up Decision Trees
+
The CART algorithm for Classification
+
The CART algorithm for Regression
+
Computing the Gini index
+
Simple Python Code to read in Data and perform Classification
+
Computing the Gini Factor
+
Entropy and the ID3 algorithm
+
Cancer Data again now with Decision Trees and other Methods
+
Another example, the moons again
+
Playing around with regions
+
Regression trees
+
Final regressor code
+
Pros and cons of trees, pros
+
Disadvantages
+
Ensemble Methods: From a Single Tree to Many Trees and Extreme Boosting, Meet the Jungle of Methods
+
An Overview of Ensemble Methods
+
Bagging
+
More bagging
+
Simple Voting Example, head or tail
+
Using the Voting Classifier
+
Please, not the moons again! Voting and Bagging
+
Bagging Examples
+
Making your own Bootstrap: Changing the Level of the Decision Tree
@@ -208,18 +212,26 @@ MathJax.Hub.Config({
-
Pros and cons of trees, pros
+
Regression trees
+
-
- White box, easy to interpret model. Some people believe that decision trees more closely mirror human decision-making than do the regression and classification approaches discussed earlier (think of support vector machines)
- Trees are very easy to explain to people. In fact, they are even easier to explain than linear regression!
- No feature normalization needed
- Tree models can handle both continuous and categorical data (Classification and Regression Trees)
- Can model nonlinear relationships
- Can model interactions between the different descriptive features
- Trees can be displayed graphically, and are easily interpreted even by a non-expert (especially if they are small)
-
+
+
# Quadratic training set + noise
+np. random. seed(42 )
+m = 200
+X = np. random. rand(m, 1 )
+y = 4 * (X - 0.5 ) ** 2
+y = y + np. random. randn(m, 1 ) / 10
+
+
+
+
from sklearn.tree import DecisionTreeRegressor
+
+tree_reg = DecisionTreeRegressor(max_depth=2 , random_state=42 )
+tree_reg. fit(X, y)
+
+
diff --git a/doc/pub/week44/html/._week44-bs035.html b/doc/pub/week44/html/._week44-bs035.html
index 9bddf3df2..9b77d22d8 100644
--- a/doc/pub/week44/html/._week44-bs035.html
+++ b/doc/pub/week44/html/._week44-bs035.html
@@ -72,46 +72,48 @@ Automatically generated HTML file from DocOnce source
('Classification tree, how to split nodes', 2, None, '___sec18'),
('Visualizing the Tree, Classification', 2, None, '___sec19'),
('Visualizing the Tree, The Moons', 2, None, '___sec20'),
- ('Algorithms for Setting up Decision Trees', 2, None, '___sec21'),
- ('The CART algorithm for Classification', 2, None, '___sec22'),
- ('The CART algorithm for Regression', 2, None, '___sec23'),
- ('Computing the Gini index', 2, None, '___sec24'),
+ ('Other ways of visualizing the trees', 2, None, '___sec21'),
+ ('Printing out as text', 2, None, '___sec22'),
+ ('Algorithms for Setting up Decision Trees', 2, None, '___sec23'),
+ ('The CART algorithm for Classification', 2, None, '___sec24'),
+ ('The CART algorithm for Regression', 2, None, '___sec25'),
+ ('Computing the Gini index', 2, None, '___sec26'),
('Simple Python Code to read in Data and perform Classification',
2,
None,
- '___sec25'),
- ('Computing the Gini Factor', 2, None, '___sec26'),
- ('Entropy and the ID3 algorithm', 2, None, '___sec27'),
+ '___sec27'),
+ ('Computing the Gini Factor', 2, None, '___sec28'),
+ ('Entropy and the ID3 algorithm', 2, None, '___sec29'),
('Cancer Data again now with Decision Trees and other Methods',
2,
None,
- '___sec28'),
- ('Another example, the moons again', 2, None, '___sec29'),
- ('Playing around with regions', 2, None, '___sec30'),
- ('Regression trees', 2, None, '___sec31'),
- ('Final regressor code', 2, None, '___sec32'),
- ('Pros and cons of trees, pros', 2, None, '___sec33'),
- ('Disadvantages', 2, None, '___sec34'),
+ '___sec30'),
+ ('Another example, the moons again', 2, None, '___sec31'),
+ ('Playing around with regions', 2, None, '___sec32'),
+ ('Regression trees', 2, None, '___sec33'),
+ ('Final regressor code', 2, None, '___sec34'),
+ ('Pros and cons of trees, pros', 2, None, '___sec35'),
+ ('Disadvantages', 2, None, '___sec36'),
('Ensemble Methods: From a Single Tree to Many Trees and Extreme '
'Boosting, Meet the Jungle of Methods',
2,
None,
- '___sec35'),
- ('An Overview of Ensemble Methods', 2, None, '___sec36'),
- ('Bagging', 2, None, '___sec37'),
- ('More bagging', 2, None, '___sec38'),
- ('Simple Voting Example, head or tail', 2, None, '___sec39'),
- ('Using the Voting Classifier', 2, None, '___sec40'),
+ '___sec37'),
+ ('An Overview of Ensemble Methods', 2, None, '___sec38'),
+ ('Bagging', 2, None, '___sec39'),
+ ('More bagging', 2, None, '___sec40'),
+ ('Simple Voting Example, head or tail', 2, None, '___sec41'),
+ ('Using the Voting Classifier', 2, None, '___sec42'),
('Please, not the moons again! Voting and Bagging',
2,
None,
- '___sec41'),
- ('Bagging Examples', 2, None, '___sec42'),
+ '___sec43'),
+ ('Bagging Examples', 2, None, '___sec44'),
('Making your own Bootstrap: Changing the Level of the Decision '
'Tree',
2,
None,
- '___sec43')]}
+ '___sec45')]}
end of tocinfo -->
@@ -170,29 +172,31 @@ MathJax.Hub.Config({
Classification tree, how to split nodes Visualizing the Tree, Classification Visualizing the Tree, The Moons
-
Algorithms for Setting up Decision Trees
-
The CART algorithm for Classification
-
The CART algorithm for Regression
-
Computing the Gini index
-
Simple Python Code to read in Data and perform Classification
-
Computing the Gini Factor
-
Entropy and the ID3 algorithm
-
Cancer Data again now with Decision Trees and other Methods
-
Another example, the moons again
-
Playing around with regions
-
Regression trees
-
Final regressor code
-
Pros and cons of trees, pros
-
Disadvantages
-
Ensemble Methods: From a Single Tree to Many Trees and Extreme Boosting, Meet the Jungle of Methods
-
An Overview of Ensemble Methods
-
Bagging
-
More bagging
-
Simple Voting Example, head or tail
-
Using the Voting Classifier
-
Please, not the moons again! Voting and Bagging
-
Bagging Examples
-
Making your own Bootstrap: Changing the Level of the Decision Tree
+
Other ways of visualizing the trees
+
Printing out as text
+
Algorithms for Setting up Decision Trees
+
The CART algorithm for Classification
+
The CART algorithm for Regression
+
Computing the Gini index
+
Simple Python Code to read in Data and perform Classification
+
Computing the Gini Factor
+
Entropy and the ID3 algorithm
+
Cancer Data again now with Decision Trees and other Methods
+
Another example, the moons again
+
Playing around with regions
+
Regression trees
+
Final regressor code
+
Pros and cons of trees, pros
+
Disadvantages
+
Ensemble Methods: From a Single Tree to Many Trees and Extreme Boosting, Meet the Jungle of Methods
+
An Overview of Ensemble Methods
+
Bagging
+
More bagging
+
Simple Voting Example, head or tail
+
Using the Voting Classifier
+
Please, not the moons again! Voting and Bagging
+
Bagging Examples
+
Making your own Bootstrap: Changing the Level of the Decision Tree
@@ -208,22 +212,81 @@ MathJax.Hub.Config({
-
Disadvantages
+
Final regressor code
+
-
- Unfortunately, trees generally do not have the same level of predictive accuracy as some of the other regression and classification approaches
- If continuous features are used the tree may become quite large and hence less interpretable
- Decision trees are prone to overfit the training data and hence do not well generalize the data if no stopping criteria or improvements like pruning, boosting or bagging are implemented
- Small changes in the data may lead to a completely different tree. This issue can be addressed by using ensemble methods like bagging, boosting or random forests
- Unbalanced datasets where some target feature values occur much more frequently than others may lead to biased trees since the frequently occurring feature values are preferred over the less frequently occurring ones.
- If the number of features is relatively large (high dimensional) and the number of instances is relatively low, the tree might overfit the data
- Features with many levels may be preferred over features with less levels since for them it is more easy to split the dataset such that the sub datasets only contain pure target feature values. This issue can be addressed by preferring for instance the information gain ratio as splitting criteria over information gain
-
+
+
from sklearn.tree import DecisionTreeRegressor
-However, by aggregating many decision trees, using methods like
-bagging, random forests, and boosting, the predictive performance of
-trees can be substantially improved.
+tree_reg1 = DecisionTreeRegressor(random_state=42 , max_depth=2 )
+tree_reg2 = DecisionTreeRegressor(random_state=42 , max_depth=3 )
+tree_reg1. fit(X, y)
+tree_reg2. fit(X, y)
+def plot_regression_predictions (tree_reg, X, y, axes= [0 , 1 , -0.2 , 1 ], ylabel= "$y$" ):
+ x1 = np. linspace(axes[0 ], axes[1 ], 500 ). reshape(-1 , 1 )
+ y_pred = tree_reg. predict(x1)
+ plt. axis(axes)
+ plt. xlabel("$x_1$" , fontsize=18 )
+ if ylabel:
+ plt. ylabel(ylabel, fontsize=18 , rotation=0 )
+ plt. plot(X, y, "b." )
+ plt. plot(x1, y_pred, "r.-" , linewidth=2 , label= r"$\hat {y} $" )
+
+plt. figure(figsize= (11 , 4 ))
+plt. subplot(121 )
+plot_regression_predictions(tree_reg1, X, y)
+for split, style in ((0.1973 , "k-" ), (0.0917 , "k--" ), (0.7718 , "k--" )):
+ plt. plot([split, split], [-0.2 , 1 ], style, linewidth=2 )
+plt. text(0.21 , 0.65 , "Depth=0" , fontsize=15 )
+plt. text(0.01 , 0.2 , "Depth=1" , fontsize=13 )
+plt. text(0.65 , 0.8 , "Depth=1" , fontsize=13 )
+plt. legend(loc= "upper center" , fontsize=18 )
+plt. title("max_depth=2" , fontsize=14 )
+
+plt. subplot(122 )
+plot_regression_predictions(tree_reg2, X, y, ylabel= None )
+for split, style in ((0.1973 , "k-" ), (0.0917 , "k--" ), (0.7718 , "k--" )):
+ plt. plot([split, split], [-0.2 , 1 ], style, linewidth=2 )
+for split in (0.0458 , 0.1298 , 0.2873 , 0.9040 ):
+ plt. plot([split, split], [-0.2 , 1 ], "k:" , linewidth=1 )
+plt. text(0.3 , 0.5 , "Depth=2" , fontsize=13 )
+plt. title("max_depth=3" , fontsize=14 )
+
+plt. show()
+
+
+
+
+
= DecisionTreeRegressor(random_state=42 )
+tree_reg2 = DecisionTreeRegressor(random_state=42 , min_samples_leaf=10 )
+tree_reg1. fit(X, y)
+tree_reg2. fit(X, y)
+
+x1 = np. linspace(0 , 1 , 500 ). reshape(-1 , 1 )
+y_pred1 = tree_reg1. predict(x1)
+y_pred2 = tree_reg2. predict(x1)
+
+plt. figure(figsize= (11 , 4 ))
+
+plt. subplot(121 )
+plt. plot(X, y, "b." )
+plt. plot(x1, y_pred1, "r.-" , linewidth=2 , label= r"$\hat {y} $" )
+plt. axis([0 , 1 , -0.2 , 1.1 ])
+plt. xlabel("$x_1$" , fontsize=18 )
+plt. ylabel("$y$" , fontsize=18 , rotation=0 )
+plt. legend(loc= "upper center" , fontsize=18 )
+plt. title("No restrictions" , fontsize=14 )
+
+plt. subplot(122 )
+plt. plot(X, y, "b." )
+plt. plot(x1, y_pred2, "r.-" , linewidth=2 , label= r"$\hat {y} $" )
+plt. axis([0 , 1 , -0.2 , 1.1 ])
+plt. xlabel("$x_1$" , fontsize=18 )
+plt. title("min_samples_leaf= {} " . format(tree_reg2. min_samples_leaf), fontsize=14 )
+
+plt. show()
+
@@ -249,6 +312,8 @@ trees can be substantially improved.
43 44 45
+
...
+
47 »
diff --git a/doc/pub/week44/html/._week44-bs036.html b/doc/pub/week44/html/._week44-bs036.html
index 8f030bcf8..11e2407d1 100644
--- a/doc/pub/week44/html/._week44-bs036.html
+++ b/doc/pub/week44/html/._week44-bs036.html
@@ -72,46 +72,48 @@ Automatically generated HTML file from DocOnce source
('Classification tree, how to split nodes', 2, None, '___sec18'),
('Visualizing the Tree, Classification', 2, None, '___sec19'),
('Visualizing the Tree, The Moons', 2, None, '___sec20'),
- ('Algorithms for Setting up Decision Trees', 2, None, '___sec21'),
- ('The CART algorithm for Classification', 2, None, '___sec22'),
- ('The CART algorithm for Regression', 2, None, '___sec23'),
- ('Computing the Gini index', 2, None, '___sec24'),
+ ('Other ways of visualizing the trees', 2, None, '___sec21'),
+ ('Printing out as text', 2, None, '___sec22'),
+ ('Algorithms for Setting up Decision Trees', 2, None, '___sec23'),
+ ('The CART algorithm for Classification', 2, None, '___sec24'),
+ ('The CART algorithm for Regression', 2, None, '___sec25'),
+ ('Computing the Gini index', 2, None, '___sec26'),
('Simple Python Code to read in Data and perform Classification',
2,
None,
- '___sec25'),
- ('Computing the Gini Factor', 2, None, '___sec26'),
- ('Entropy and the ID3 algorithm', 2, None, '___sec27'),
+ '___sec27'),
+ ('Computing the Gini Factor', 2, None, '___sec28'),
+ ('Entropy and the ID3 algorithm', 2, None, '___sec29'),
('Cancer Data again now with Decision Trees and other Methods',
2,
None,
- '___sec28'),
- ('Another example, the moons again', 2, None, '___sec29'),
- ('Playing around with regions', 2, None, '___sec30'),
- ('Regression trees', 2, None, '___sec31'),
- ('Final regressor code', 2, None, '___sec32'),
- ('Pros and cons of trees, pros', 2, None, '___sec33'),
- ('Disadvantages', 2, None, '___sec34'),
+ '___sec30'),
+ ('Another example, the moons again', 2, None, '___sec31'),
+ ('Playing around with regions', 2, None, '___sec32'),
+ ('Regression trees', 2, None, '___sec33'),
+ ('Final regressor code', 2, None, '___sec34'),
+ ('Pros and cons of trees, pros', 2, None, '___sec35'),
+ ('Disadvantages', 2, None, '___sec36'),
('Ensemble Methods: From a Single Tree to Many Trees and Extreme '
'Boosting, Meet the Jungle of Methods',
2,
None,
- '___sec35'),
- ('An Overview of Ensemble Methods', 2, None, '___sec36'),
- ('Bagging', 2, None, '___sec37'),
- ('More bagging', 2, None, '___sec38'),
- ('Simple Voting Example, head or tail', 2, None, '___sec39'),
- ('Using the Voting Classifier', 2, None, '___sec40'),
+ '___sec37'),
+ ('An Overview of Ensemble Methods', 2, None, '___sec38'),
+ ('Bagging', 2, None, '___sec39'),
+ ('More bagging', 2, None, '___sec40'),
+ ('Simple Voting Example, head or tail', 2, None, '___sec41'),
+ ('Using the Voting Classifier', 2, None, '___sec42'),
('Please, not the moons again! Voting and Bagging',
2,
None,
- '___sec41'),
- ('Bagging Examples', 2, None, '___sec42'),
+ '___sec43'),
+ ('Bagging Examples', 2, None, '___sec44'),
('Making your own Bootstrap: Changing the Level of the Decision '
'Tree',
2,
None,
- '___sec43')]}
+ '___sec45')]}
end of tocinfo -->
@@ -170,29 +172,31 @@ MathJax.Hub.Config({
Classification tree, how to split nodes Visualizing the Tree, Classification Visualizing the Tree, The Moons
-
Algorithms for Setting up Decision Trees
-
The CART algorithm for Classification
-
The CART algorithm for Regression
-
Computing the Gini index
-
Simple Python Code to read in Data and perform Classification
-
Computing the Gini Factor
-
Entropy and the ID3 algorithm
-
Cancer Data again now with Decision Trees and other Methods
-
Another example, the moons again
-
Playing around with regions
-
Regression trees
-
Final regressor code
-
Pros and cons of trees, pros
-
Disadvantages
-
Ensemble Methods: From a Single Tree to Many Trees and Extreme Boosting, Meet the Jungle of Methods
-
An Overview of Ensemble Methods
-
Bagging
-
More bagging
-
Simple Voting Example, head or tail
-
Using the Voting Classifier
-
Please, not the moons again! Voting and Bagging
-
Bagging Examples
-
Making your own Bootstrap: Changing the Level of the Decision Tree
+
Other ways of visualizing the trees
+
Printing out as text
+
Algorithms for Setting up Decision Trees
+
The CART algorithm for Classification
+
The CART algorithm for Regression
+
Computing the Gini index
+
Simple Python Code to read in Data and perform Classification
+
Computing the Gini Factor
+
Entropy and the ID3 algorithm
+
Cancer Data again now with Decision Trees and other Methods
+
Another example, the moons again
+
Playing around with regions
+
Regression trees
+
Final regressor code
+
Pros and cons of trees, pros
+
Disadvantages
+
Ensemble Methods: From a Single Tree to Many Trees and Extreme Boosting, Meet the Jungle of Methods
+
An Overview of Ensemble Methods
+
Bagging
+
More bagging
+
Simple Voting Example, head or tail
+
Using the Voting Classifier
+
Please, not the moons again! Voting and Bagging
+
Bagging Examples
+
Making your own Bootstrap: Changing the Level of the Decision Tree
@@ -208,31 +212,18 @@ MathJax.Hub.Config({
-
Ensemble Methods: From a Single Tree to Many Trees and Extreme Boosting, Meet the Jungle of Methods
+
Pros and cons of trees, pros
-
-As stated above and seen in many of the examples discussed here about
-a single decision tree, we often end up overfitting our training
-data. This normally means that we have a high variance. Can we reduce
-the variance of a statistical learning method?
+
+ White box, easy to interpret model. Some people believe that decision trees more closely mirror human decision-making than do the regression and classification approaches discussed earlier (think of support vector machines)
+ Trees are very easy to explain to people. In fact, they are even easier to explain than linear regression!
+ No feature normalization needed
+ Tree models can handle both continuous and categorical data (Classification and Regression Trees)
+ Can model nonlinear relationships
+ Can model interactions between the different descriptive features
+ Trees can be displayed graphically, and are easily interpreted even by a non-expert (especially if they are small)
+
-
-This leads us to a set of different methods that can combine different
-machine learning algorithms or just use one of them to construct
-forests and jungles of trees, homogeneous ones or heterogenous
-ones. These methods are recognized by different names which we will
-try to explain here. These are
-
-
- Voting classifiers
- Bagging and Pasting
- Random forests
- Boosting methods, from adaptive to Extreme Gradient Boosting (XGBoost)
-
-
-We discuss these methods here.
-
-
diff --git a/doc/pub/week44/html/._week44-bs037.html b/doc/pub/week44/html/._week44-bs037.html
index fa6e4ecaa..f173b4179 100644
--- a/doc/pub/week44/html/._week44-bs037.html
+++ b/doc/pub/week44/html/._week44-bs037.html
@@ -72,46 +72,48 @@ Automatically generated HTML file from DocOnce source
('Classification tree, how to split nodes', 2, None, '___sec18'),
('Visualizing the Tree, Classification', 2, None, '___sec19'),
('Visualizing the Tree, The Moons', 2, None, '___sec20'),
- ('Algorithms for Setting up Decision Trees', 2, None, '___sec21'),
- ('The CART algorithm for Classification', 2, None, '___sec22'),
- ('The CART algorithm for Regression', 2, None, '___sec23'),
- ('Computing the Gini index', 2, None, '___sec24'),
+ ('Other ways of visualizing the trees', 2, None, '___sec21'),
+ ('Printing out as text', 2, None, '___sec22'),
+ ('Algorithms for Setting up Decision Trees', 2, None, '___sec23'),
+ ('The CART algorithm for Classification', 2, None, '___sec24'),
+ ('The CART algorithm for Regression', 2, None, '___sec25'),
+ ('Computing the Gini index', 2, None, '___sec26'),
('Simple Python Code to read in Data and perform Classification',
2,
None,
- '___sec25'),
- ('Computing the Gini Factor', 2, None, '___sec26'),
- ('Entropy and the ID3 algorithm', 2, None, '___sec27'),
+ '___sec27'),
+ ('Computing the Gini Factor', 2, None, '___sec28'),
+ ('Entropy and the ID3 algorithm', 2, None, '___sec29'),
('Cancer Data again now with Decision Trees and other Methods',
2,
None,
- '___sec28'),
- ('Another example, the moons again', 2, None, '___sec29'),
- ('Playing around with regions', 2, None, '___sec30'),
- ('Regression trees', 2, None, '___sec31'),
- ('Final regressor code', 2, None, '___sec32'),
- ('Pros and cons of trees, pros', 2, None, '___sec33'),
- ('Disadvantages', 2, None, '___sec34'),
+ '___sec30'),
+ ('Another example, the moons again', 2, None, '___sec31'),
+ ('Playing around with regions', 2, None, '___sec32'),
+ ('Regression trees', 2, None, '___sec33'),
+ ('Final regressor code', 2, None, '___sec34'),
+ ('Pros and cons of trees, pros', 2, None, '___sec35'),
+ ('Disadvantages', 2, None, '___sec36'),
('Ensemble Methods: From a Single Tree to Many Trees and Extreme '
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2,
None,
- '___sec35'),
- ('An Overview of Ensemble Methods', 2, None, '___sec36'),
- ('Bagging', 2, None, '___sec37'),
- ('More bagging', 2, None, '___sec38'),
- ('Simple Voting Example, head or tail', 2, None, '___sec39'),
- ('Using the Voting Classifier', 2, None, '___sec40'),
+ '___sec37'),
+ ('An Overview of Ensemble Methods', 2, None, '___sec38'),
+ ('Bagging', 2, None, '___sec39'),
+ ('More bagging', 2, None, '___sec40'),
+ ('Simple Voting Example, head or tail', 2, None, '___sec41'),
+ ('Using the Voting Classifier', 2, None, '___sec42'),
('Please, not the moons again! Voting and Bagging',
2,
None,
- '___sec41'),
- ('Bagging Examples', 2, None, '___sec42'),
+ '___sec43'),
+ ('Bagging Examples', 2, None, '___sec44'),
('Making your own Bootstrap: Changing the Level of the Decision '
'Tree',
2,
None,
- '___sec43')]}
+ '___sec45')]}
end of tocinfo -->
@@ -170,29 +172,31 @@ MathJax.Hub.Config({
Classification tree, how to split nodes Visualizing the Tree, Classification Visualizing the Tree, The Moons
-
Algorithms for Setting up Decision Trees
-
The CART algorithm for Classification
-
The CART algorithm for Regression
-
Computing the Gini index
-
Simple Python Code to read in Data and perform Classification
-
Computing the Gini Factor
-
Entropy and the ID3 algorithm
-
Cancer Data again now with Decision Trees and other Methods
-
Another example, the moons again
-
Playing around with regions
-
Regression trees
-
Final regressor code
-
Pros and cons of trees, pros
-
Disadvantages
-
Ensemble Methods: From a Single Tree to Many Trees and Extreme Boosting, Meet the Jungle of Methods
-
An Overview of Ensemble Methods
-
Bagging
-
More bagging
-
Simple Voting Example, head or tail
-
Using the Voting Classifier
-
Please, not the moons again! Voting and Bagging
-
Bagging Examples
-
Making your own Bootstrap: Changing the Level of the Decision Tree
+
Other ways of visualizing the trees
+
Printing out as text
+
Algorithms for Setting up Decision Trees
+
The CART algorithm for Classification
+
The CART algorithm for Regression
+
Computing the Gini index
+
Simple Python Code to read in Data and perform Classification
+
Computing the Gini Factor
+
Entropy and the ID3 algorithm
+
Cancer Data again now with Decision Trees and other Methods
+
Another example, the moons again
+
Playing around with regions
+
Regression trees
+
Final regressor code
+
Pros and cons of trees, pros
+
Disadvantages
+
Ensemble Methods: From a Single Tree to Many Trees and Extreme Boosting, Meet the Jungle of Methods
+
An Overview of Ensemble Methods
+
Bagging
+
More bagging
+
Simple Voting Example, head or tail
+
Using the Voting Classifier
+
Please, not the moons again! Voting and Bagging
+
Bagging Examples
+
Making your own Bootstrap: Changing the Level of the Decision Tree
@@ -208,10 +212,21 @@ MathJax.Hub.Config({
-
An Overview of Ensemble Methods
+
Disadvantages
-
-
+
+ Unfortunately, trees generally do not have the same level of predictive accuracy as some of the other regression and classification approaches
+ If continuous features are used the tree may become quite large and hence less interpretable
+ Decision trees are prone to overfit the training data and hence do not well generalize the data if no stopping criteria or improvements like pruning, boosting or bagging are implemented
+ Small changes in the data may lead to a completely different tree. This issue can be addressed by using ensemble methods like bagging, boosting or random forests
+ Unbalanced datasets where some target feature values occur much more frequently than others may lead to biased trees since the frequently occurring feature values are preferred over the less frequently occurring ones.
+ If the number of features is relatively large (high dimensional) and the number of instances is relatively low, the tree might overfit the data
+ Features with many levels may be preferred over features with less levels since for them it is more easy to split the dataset such that the sub datasets only contain pure target feature values. This issue can be addressed by preferring for instance the information gain ratio as splitting criteria over information gain
+
+
+However, by aggregating many decision trees, using methods like
+bagging, random forests, and boosting, the predictive performance of
+trees can be substantially improved.
@@ -236,6 +251,8 @@ MathJax.Hub.Config({
43 44 45
+
46
+
47 »
diff --git a/doc/pub/week44/html/._week44-bs038.html b/doc/pub/week44/html/._week44-bs038.html
index 758626c38..fa0edd371 100644
--- a/doc/pub/week44/html/._week44-bs038.html
+++ b/doc/pub/week44/html/._week44-bs038.html
@@ -72,46 +72,48 @@ Automatically generated HTML file from DocOnce source
('Classification tree, how to split nodes', 2, None, '___sec18'),
('Visualizing the Tree, Classification', 2, None, '___sec19'),
('Visualizing the Tree, The Moons', 2, None, '___sec20'),
- ('Algorithms for Setting up Decision Trees', 2, None, '___sec21'),
- ('The CART algorithm for Classification', 2, None, '___sec22'),
- ('The CART algorithm for Regression', 2, None, '___sec23'),
- ('Computing the Gini index', 2, None, '___sec24'),
+ ('Other ways of visualizing the trees', 2, None, '___sec21'),
+ ('Printing out as text', 2, None, '___sec22'),
+ ('Algorithms for Setting up Decision Trees', 2, None, '___sec23'),
+ ('The CART algorithm for Classification', 2, None, '___sec24'),
+ ('The CART algorithm for Regression', 2, None, '___sec25'),
+ ('Computing the Gini index', 2, None, '___sec26'),
('Simple Python Code to read in Data and perform Classification',
2,
None,
- '___sec25'),
- ('Computing the Gini Factor', 2, None, '___sec26'),
- ('Entropy and the ID3 algorithm', 2, None, '___sec27'),
+ '___sec27'),
+ ('Computing the Gini Factor', 2, None, '___sec28'),
+ ('Entropy and the ID3 algorithm', 2, None, '___sec29'),
('Cancer Data again now with Decision Trees and other Methods',
2,
None,
- '___sec28'),
- ('Another example, the moons again', 2, None, '___sec29'),
- ('Playing around with regions', 2, None, '___sec30'),
- ('Regression trees', 2, None, '___sec31'),
- ('Final regressor code', 2, None, '___sec32'),
- ('Pros and cons of trees, pros', 2, None, '___sec33'),
- ('Disadvantages', 2, None, '___sec34'),
+ '___sec30'),
+ ('Another example, the moons again', 2, None, '___sec31'),
+ ('Playing around with regions', 2, None, '___sec32'),
+ ('Regression trees', 2, None, '___sec33'),
+ ('Final regressor code', 2, None, '___sec34'),
+ ('Pros and cons of trees, pros', 2, None, '___sec35'),
+ ('Disadvantages', 2, None, '___sec36'),
('Ensemble Methods: From a Single Tree to Many Trees and Extreme '
'Boosting, Meet the Jungle of Methods',
2,
None,
- '___sec35'),
- ('An Overview of Ensemble Methods', 2, None, '___sec36'),
- ('Bagging', 2, None, '___sec37'),
- ('More bagging', 2, None, '___sec38'),
- ('Simple Voting Example, head or tail', 2, None, '___sec39'),
- ('Using the Voting Classifier', 2, None, '___sec40'),
+ '___sec37'),
+ ('An Overview of Ensemble Methods', 2, None, '___sec38'),
+ ('Bagging', 2, None, '___sec39'),
+ ('More bagging', 2, None, '___sec40'),
+ ('Simple Voting Example, head or tail', 2, None, '___sec41'),
+ ('Using the Voting Classifier', 2, None, '___sec42'),
('Please, not the moons again! Voting and Bagging',
2,
None,
- '___sec41'),
- ('Bagging Examples', 2, None, '___sec42'),
+ '___sec43'),
+ ('Bagging Examples', 2, None, '___sec44'),
('Making your own Bootstrap: Changing the Level of the Decision '
'Tree',
2,
None,
- '___sec43')]}
+ '___sec45')]}
end of tocinfo -->
@@ -170,29 +172,31 @@ MathJax.Hub.Config({
Classification tree, how to split nodes Visualizing the Tree, Classification Visualizing the Tree, The Moons
-
Algorithms for Setting up Decision Trees
-
The CART algorithm for Classification
-
The CART algorithm for Regression
-
Computing the Gini index
-
Simple Python Code to read in Data and perform Classification
-
Computing the Gini Factor
-
Entropy and the ID3 algorithm
-
Cancer Data again now with Decision Trees and other Methods
-
Another example, the moons again
-
Playing around with regions
-
Regression trees
-
Final regressor code
-
Pros and cons of trees, pros
-
Disadvantages
-
Ensemble Methods: From a Single Tree to Many Trees and Extreme Boosting, Meet the Jungle of Methods
-
An Overview of Ensemble Methods
-
Bagging
-
More bagging
-
Simple Voting Example, head or tail
-
Using the Voting Classifier
-
Please, not the moons again! Voting and Bagging
-
Bagging Examples
-
Making your own Bootstrap: Changing the Level of the Decision Tree
+
Other ways of visualizing the trees
+
Printing out as text
+
Algorithms for Setting up Decision Trees
+
The CART algorithm for Classification
+
The CART algorithm for Regression
+
Computing the Gini index
+
Simple Python Code to read in Data and perform Classification
+
Computing the Gini Factor
+
Entropy and the ID3 algorithm
+
Cancer Data again now with Decision Trees and other Methods
+
Another example, the moons again
+
Playing around with regions
+
Regression trees
+
Final regressor code
+
Pros and cons of trees, pros
+
Disadvantages
+
Ensemble Methods: From a Single Tree to Many Trees and Extreme Boosting, Meet the Jungle of Methods
+
An Overview of Ensemble Methods
+
Bagging
+
More bagging
+
Simple Voting Example, head or tail
+
Using the Voting Classifier
+
Please, not the moons again! Voting and Bagging
+
Bagging Examples
+
Making your own Bootstrap: Changing the Level of the Decision Tree
@@ -208,21 +212,29 @@ MathJax.Hub.Config({
-
Bagging
+
Ensemble Methods: From a Single Tree to Many Trees and Extreme Boosting, Meet the Jungle of Methods
-The plain decision trees suffer from high
-variance. This means that if we split the training data into two parts
-at random, and fit a decision tree to both halves, the results that we
-get could be quite different. In contrast, a procedure with low
-variance will yield similar results if applied repeatedly to distinct
-data sets; linear regression tends to have low variance, if the ratio
-of \( n \) to \( p \) is moderately large.
+As stated above and seen in many of the examples discussed here about
+a single decision tree, we often end up overfitting our training
+data. This normally means that we have a high variance. Can we reduce
+the variance of a statistical learning method?
-Bootstrap aggregation , or just bagging , is a
-general-purpose procedure for reducing the variance of a statistical
-learning method.
+This leads us to a set of different methods that can combine different
+machine learning algorithms or just use one of them to construct
+forests and jungles of trees, homogeneous ones or heterogenous
+ones. These methods are recognized by different names which we will
+try to explain here. These are
+
+
+ Voting classifiers
+ Bagging and Pasting
+ Random forests
+ Boosting methods, from adaptive to Extreme Gradient Boosting (XGBoost)
+
+
+We discuss these methods here.
@@ -246,6 +258,8 @@ learning method.
43 44 45
+
46
+
47 »
diff --git a/doc/pub/week44/html/._week44-bs039.html b/doc/pub/week44/html/._week44-bs039.html
index a52940049..825a9ffa7 100644
--- a/doc/pub/week44/html/._week44-bs039.html
+++ b/doc/pub/week44/html/._week44-bs039.html
@@ -72,46 +72,48 @@ Automatically generated HTML file from DocOnce source
('Classification tree, how to split nodes', 2, None, '___sec18'),
('Visualizing the Tree, Classification', 2, None, '___sec19'),
('Visualizing the Tree, The Moons', 2, None, '___sec20'),
- ('Algorithms for Setting up Decision Trees', 2, None, '___sec21'),
- ('The CART algorithm for Classification', 2, None, '___sec22'),
- ('The CART algorithm for Regression', 2, None, '___sec23'),
- ('Computing the Gini index', 2, None, '___sec24'),
+ ('Other ways of visualizing the trees', 2, None, '___sec21'),
+ ('Printing out as text', 2, None, '___sec22'),
+ ('Algorithms for Setting up Decision Trees', 2, None, '___sec23'),
+ ('The CART algorithm for Classification', 2, None, '___sec24'),
+ ('The CART algorithm for Regression', 2, None, '___sec25'),
+ ('Computing the Gini index', 2, None, '___sec26'),
('Simple Python Code to read in Data and perform Classification',
2,
None,
- '___sec25'),
- ('Computing the Gini Factor', 2, None, '___sec26'),
- ('Entropy and the ID3 algorithm', 2, None, '___sec27'),
+ '___sec27'),
+ ('Computing the Gini Factor', 2, None, '___sec28'),
+ ('Entropy and the ID3 algorithm', 2, None, '___sec29'),
('Cancer Data again now with Decision Trees and other Methods',
2,
None,
- '___sec28'),
- ('Another example, the moons again', 2, None, '___sec29'),
- ('Playing around with regions', 2, None, '___sec30'),
- ('Regression trees', 2, None, '___sec31'),
- ('Final regressor code', 2, None, '___sec32'),
- ('Pros and cons of trees, pros', 2, None, '___sec33'),
- ('Disadvantages', 2, None, '___sec34'),
+ '___sec30'),
+ ('Another example, the moons again', 2, None, '___sec31'),
+ ('Playing around with regions', 2, None, '___sec32'),
+ ('Regression trees', 2, None, '___sec33'),
+ ('Final regressor code', 2, None, '___sec34'),
+ ('Pros and cons of trees, pros', 2, None, '___sec35'),
+ ('Disadvantages', 2, None, '___sec36'),
('Ensemble Methods: From a Single Tree to Many Trees and Extreme '
'Boosting, Meet the Jungle of Methods',
2,
None,
- '___sec35'),
- ('An Overview of Ensemble Methods', 2, None, '___sec36'),
- ('Bagging', 2, None, '___sec37'),
- ('More bagging', 2, None, '___sec38'),
- ('Simple Voting Example, head or tail', 2, None, '___sec39'),
- ('Using the Voting Classifier', 2, None, '___sec40'),
+ '___sec37'),
+ ('An Overview of Ensemble Methods', 2, None, '___sec38'),
+ ('Bagging', 2, None, '___sec39'),
+ ('More bagging', 2, None, '___sec40'),
+ ('Simple Voting Example, head or tail', 2, None, '___sec41'),
+ ('Using the Voting Classifier', 2, None, '___sec42'),
('Please, not the moons again! Voting and Bagging',
2,
None,
- '___sec41'),
- ('Bagging Examples', 2, None, '___sec42'),
+ '___sec43'),
+ ('Bagging Examples', 2, None, '___sec44'),
('Making your own Bootstrap: Changing the Level of the Decision '
'Tree',
2,
None,
- '___sec43')]}
+ '___sec45')]}
end of tocinfo -->
@@ -170,29 +172,31 @@ MathJax.Hub.Config({
Classification tree, how to split nodes Visualizing the Tree, Classification Visualizing the Tree, The Moons
-
Algorithms for Setting up Decision Trees
-
The CART algorithm for Classification
-
The CART algorithm for Regression
-
Computing the Gini index
-
Simple Python Code to read in Data and perform Classification
-
Computing the Gini Factor
-
Entropy and the ID3 algorithm
-
Cancer Data again now with Decision Trees and other Methods
-
Another example, the moons again
-
Playing around with regions
-
Regression trees
-
Final regressor code
-
Pros and cons of trees, pros
-
Disadvantages
-
Ensemble Methods: From a Single Tree to Many Trees and Extreme Boosting, Meet the Jungle of Methods
-
An Overview of Ensemble Methods
-
Bagging
-
More bagging
-
Simple Voting Example, head or tail
-
Using the Voting Classifier
-
Please, not the moons again! Voting and Bagging
-
Bagging Examples
-
Making your own Bootstrap: Changing the Level of the Decision Tree
+
Other ways of visualizing the trees
+
Printing out as text
+
Algorithms for Setting up Decision Trees
+
The CART algorithm for Classification
+
The CART algorithm for Regression
+
Computing the Gini index
+
Simple Python Code to read in Data and perform Classification
+
Computing the Gini Factor
+
Entropy and the ID3 algorithm
+
Cancer Data again now with Decision Trees and other Methods
+
Another example, the moons again
+
Playing around with regions
+
Regression trees
+
Final regressor code
+
Pros and cons of trees, pros
+
Disadvantages
+
Ensemble Methods: From a Single Tree to Many Trees and Extreme Boosting, Meet the Jungle of Methods
+
An Overview of Ensemble Methods
+
Bagging
+
More bagging
+
Simple Voting Example, head or tail
+
Using the Voting Classifier
+
Please, not the moons again! Voting and Bagging
+
Bagging Examples
+
Making your own Bootstrap: Changing the Level of the Decision Tree
@@ -208,31 +212,10 @@ MathJax.Hub.Config({
-
More bagging
+
An Overview of Ensemble Methods
-Bagging typically results in improved accuracy
-over prediction using a single tree. Unfortunately, however, it can be
-difficult to interpret the resulting model. Recall that one of the
-advantages of decision trees is the attractive and easily interpreted
-diagram that results.
-
-
-However, when we bag a large number of trees, it is no longer
-possible to represent the resulting statistical learning procedure
-using a single tree, and it is no longer clear which variables are
-most important to the procedure. Thus, bagging improves prediction
-accuracy at the expense of interpretability. Although the collection
-of bagged trees is much more difficult to interpret than a single
-tree, one can obtain an overall summary of the importance of each
-predictor using the MSE (for bagging regression trees) or the Gini
-index (for bagging classification trees). In the case of bagging
-regression trees, we can record the total amount that the MSE is
-decreased due to splits over a given predictor, averaged over all \( B \) possible
-trees. A large value indicates an important predictor. Similarly, in
-the context of bagging classification trees, we can add up the total
-amount that the Gini index is decreased by splits over a given
-predictor, averaged over all \( B \) trees.
+
@@ -255,6 +238,8 @@ predictor, averaged over all \( B \) trees.
43 44 45
+
46
+
47 »
diff --git a/doc/pub/week44/html/._week44-bs040.html b/doc/pub/week44/html/._week44-bs040.html
index 632af325f..5b55d3130 100644
--- a/doc/pub/week44/html/._week44-bs040.html
+++ b/doc/pub/week44/html/._week44-bs040.html
@@ -72,46 +72,48 @@ Automatically generated HTML file from DocOnce source
('Classification tree, how to split nodes', 2, None, '___sec18'),
('Visualizing the Tree, Classification', 2, None, '___sec19'),
('Visualizing the Tree, The Moons', 2, None, '___sec20'),
- ('Algorithms for Setting up Decision Trees', 2, None, '___sec21'),
- ('The CART algorithm for Classification', 2, None, '___sec22'),
- ('The CART algorithm for Regression', 2, None, '___sec23'),
- ('Computing the Gini index', 2, None, '___sec24'),
+ ('Other ways of visualizing the trees', 2, None, '___sec21'),
+ ('Printing out as text', 2, None, '___sec22'),
+ ('Algorithms for Setting up Decision Trees', 2, None, '___sec23'),
+ ('The CART algorithm for Classification', 2, None, '___sec24'),
+ ('The CART algorithm for Regression', 2, None, '___sec25'),
+ ('Computing the Gini index', 2, None, '___sec26'),
('Simple Python Code to read in Data and perform Classification',
2,
None,
- '___sec25'),
- ('Computing the Gini Factor', 2, None, '___sec26'),
- ('Entropy and the ID3 algorithm', 2, None, '___sec27'),
+ '___sec27'),
+ ('Computing the Gini Factor', 2, None, '___sec28'),
+ ('Entropy and the ID3 algorithm', 2, None, '___sec29'),
('Cancer Data again now with Decision Trees and other Methods',
2,
None,
- '___sec28'),
- ('Another example, the moons again', 2, None, '___sec29'),
- ('Playing around with regions', 2, None, '___sec30'),
- ('Regression trees', 2, None, '___sec31'),
- ('Final regressor code', 2, None, '___sec32'),
- ('Pros and cons of trees, pros', 2, None, '___sec33'),
- ('Disadvantages', 2, None, '___sec34'),
+ '___sec30'),
+ ('Another example, the moons again', 2, None, '___sec31'),
+ ('Playing around with regions', 2, None, '___sec32'),
+ ('Regression trees', 2, None, '___sec33'),
+ ('Final regressor code', 2, None, '___sec34'),
+ ('Pros and cons of trees, pros', 2, None, '___sec35'),
+ ('Disadvantages', 2, None, '___sec36'),
('Ensemble Methods: From a Single Tree to Many Trees and Extreme '
'Boosting, Meet the Jungle of Methods',
2,
None,
- '___sec35'),
- ('An Overview of Ensemble Methods', 2, None, '___sec36'),
- ('Bagging', 2, None, '___sec37'),
- ('More bagging', 2, None, '___sec38'),
- ('Simple Voting Example, head or tail', 2, None, '___sec39'),
- ('Using the Voting Classifier', 2, None, '___sec40'),
+ '___sec37'),
+ ('An Overview of Ensemble Methods', 2, None, '___sec38'),
+ ('Bagging', 2, None, '___sec39'),
+ ('More bagging', 2, None, '___sec40'),
+ ('Simple Voting Example, head or tail', 2, None, '___sec41'),
+ ('Using the Voting Classifier', 2, None, '___sec42'),
('Please, not the moons again! Voting and Bagging',
2,
None,
- '___sec41'),
- ('Bagging Examples', 2, None, '___sec42'),
+ '___sec43'),
+ ('Bagging Examples', 2, None, '___sec44'),
('Making your own Bootstrap: Changing the Level of the Decision '
'Tree',
2,
None,
- '___sec43')]}
+ '___sec45')]}
end of tocinfo -->
@@ -170,29 +172,31 @@ MathJax.Hub.Config({
Classification tree, how to split nodes Visualizing the Tree, Classification Visualizing the Tree, The Moons
-
Algorithms for Setting up Decision Trees
-
The CART algorithm for Classification
-
The CART algorithm for Regression
-
Computing the Gini index
-
Simple Python Code to read in Data and perform Classification
-
Computing the Gini Factor
-
Entropy and the ID3 algorithm
-
Cancer Data again now with Decision Trees and other Methods
-
Another example, the moons again
-
Playing around with regions
-
Regression trees
-
Final regressor code
-
Pros and cons of trees, pros
-
Disadvantages
-
Ensemble Methods: From a Single Tree to Many Trees and Extreme Boosting, Meet the Jungle of Methods
-
An Overview of Ensemble Methods
-
Bagging
-
More bagging
-
Simple Voting Example, head or tail
-
Using the Voting Classifier
-
Please, not the moons again! Voting and Bagging
-
Bagging Examples
-
Making your own Bootstrap: Changing the Level of the Decision Tree
+
Other ways of visualizing the trees
+
Printing out as text
+
Algorithms for Setting up Decision Trees
+
The CART algorithm for Classification
+
The CART algorithm for Regression
+
Computing the Gini index
+
Simple Python Code to read in Data and perform Classification
+
Computing the Gini Factor
+
Entropy and the ID3 algorithm
+
Cancer Data again now with Decision Trees and other Methods
+
Another example, the moons again
+
Playing around with regions
+
Regression trees
+
Final regressor code
+
Pros and cons of trees, pros
+
Disadvantages
+
Ensemble Methods: From a Single Tree to Many Trees and Extreme Boosting, Meet the Jungle of Methods
+
An Overview of Ensemble Methods
+
Bagging
+
More bagging
+
Simple Voting Example, head or tail
+
Using the Voting Classifier
+
Please, not the moons again! Voting and Bagging
+
Bagging Examples
+
Making your own Bootstrap: Changing the Level of the Decision Tree
@@ -208,24 +212,22 @@ MathJax.Hub.Config({
-
Simple Voting Example, head or tail
-
+
Bagging
+
+
+The plain decision trees suffer from high
+variance. This means that if we split the training data into two parts
+at random, and fit a decision tree to both halves, the results that we
+get could be quite different. In contrast, a procedure with low
+variance will yield similar results if applied repeatedly to distinct
+data sets; linear regression tends to have low variance, if the ratio
+of \( n \) to \( p \) is moderately large.
+
+
+Bootstrap aggregation , or just bagging , is a
+general-purpose procedure for reducing the variance of a statistical
+learning method.
-
-
= 0.51
-coin_tosses = (np. random. rand(10000 , 10 ) < heads_proba). astype(np. int32)
-cumulative_heads_ratio = np. cumsum(coin_tosses, axis=0 ) / np. arange(1 , 10001 ). reshape(-1 , 1 )
-plt. figure(figsize= (8 ,3.5 ))
-plt. plot(cumulative_heads_ratio)
-plt. plot([0 , 10000 ], [0.51 , 0.51 ], "k--" , linewidth=2 , label= "51%" )
-plt. plot([0 , 10000 ], [0.5 , 0.5 ], "k-" , label= "50%" )
-plt. xlabel("Number of coin tosses" )
-plt. ylabel("Heads ratio" )
-plt. legend(loc= "lower right" )
-plt. axis([0 , 10000 , 0.42 , 0.58 ])
-save_fig("votingsimple" )
-plt. show()
-
@@ -246,6 +248,8 @@ plt. show()
43 44 45
+
46
+
47 »
diff --git a/doc/pub/week44/html/._week44-bs041.html b/doc/pub/week44/html/._week44-bs041.html
index 937372cdf..9c8afc1ef 100644
--- a/doc/pub/week44/html/._week44-bs041.html
+++ b/doc/pub/week44/html/._week44-bs041.html
@@ -72,46 +72,48 @@ Automatically generated HTML file from DocOnce source
('Classification tree, how to split nodes', 2, None, '___sec18'),
('Visualizing the Tree, Classification', 2, None, '___sec19'),
('Visualizing the Tree, The Moons', 2, None, '___sec20'),
- ('Algorithms for Setting up Decision Trees', 2, None, '___sec21'),
- ('The CART algorithm for Classification', 2, None, '___sec22'),
- ('The CART algorithm for Regression', 2, None, '___sec23'),
- ('Computing the Gini index', 2, None, '___sec24'),
+ ('Other ways of visualizing the trees', 2, None, '___sec21'),
+ ('Printing out as text', 2, None, '___sec22'),
+ ('Algorithms for Setting up Decision Trees', 2, None, '___sec23'),
+ ('The CART algorithm for Classification', 2, None, '___sec24'),
+ ('The CART algorithm for Regression', 2, None, '___sec25'),
+ ('Computing the Gini index', 2, None, '___sec26'),
('Simple Python Code to read in Data and perform Classification',
2,
None,
- '___sec25'),
- ('Computing the Gini Factor', 2, None, '___sec26'),
- ('Entropy and the ID3 algorithm', 2, None, '___sec27'),
+ '___sec27'),
+ ('Computing the Gini Factor', 2, None, '___sec28'),
+ ('Entropy and the ID3 algorithm', 2, None, '___sec29'),
('Cancer Data again now with Decision Trees and other Methods',
2,
None,
- '___sec28'),
- ('Another example, the moons again', 2, None, '___sec29'),
- ('Playing around with regions', 2, None, '___sec30'),
- ('Regression trees', 2, None, '___sec31'),
- ('Final regressor code', 2, None, '___sec32'),
- ('Pros and cons of trees, pros', 2, None, '___sec33'),
- ('Disadvantages', 2, None, '___sec34'),
+ '___sec30'),
+ ('Another example, the moons again', 2, None, '___sec31'),
+ ('Playing around with regions', 2, None, '___sec32'),
+ ('Regression trees', 2, None, '___sec33'),
+ ('Final regressor code', 2, None, '___sec34'),
+ ('Pros and cons of trees, pros', 2, None, '___sec35'),
+ ('Disadvantages', 2, None, '___sec36'),
('Ensemble Methods: From a Single Tree to Many Trees and Extreme '
'Boosting, Meet the Jungle of Methods',
2,
None,
- '___sec35'),
- ('An Overview of Ensemble Methods', 2, None, '___sec36'),
- ('Bagging', 2, None, '___sec37'),
- ('More bagging', 2, None, '___sec38'),
- ('Simple Voting Example, head or tail', 2, None, '___sec39'),
- ('Using the Voting Classifier', 2, None, '___sec40'),
+ '___sec37'),
+ ('An Overview of Ensemble Methods', 2, None, '___sec38'),
+ ('Bagging', 2, None, '___sec39'),
+ ('More bagging', 2, None, '___sec40'),
+ ('Simple Voting Example, head or tail', 2, None, '___sec41'),
+ ('Using the Voting Classifier', 2, None, '___sec42'),
('Please, not the moons again! Voting and Bagging',
2,
None,
- '___sec41'),
- ('Bagging Examples', 2, None, '___sec42'),
+ '___sec43'),
+ ('Bagging Examples', 2, None, '___sec44'),
('Making your own Bootstrap: Changing the Level of the Decision '
'Tree',
2,
None,
- '___sec43')]}
+ '___sec45')]}
end of tocinfo -->
@@ -170,29 +172,31 @@ MathJax.Hub.Config({
Classification tree, how to split nodes Visualizing the Tree, Classification Visualizing the Tree, The Moons
-
Algorithms for Setting up Decision Trees
-
The CART algorithm for Classification
-
The CART algorithm for Regression
-
Computing the Gini index
-
Simple Python Code to read in Data and perform Classification
-
Computing the Gini Factor
-
Entropy and the ID3 algorithm
-
Cancer Data again now with Decision Trees and other Methods
-
Another example, the moons again
-
Playing around with regions
-
Regression trees
-
Final regressor code
-
Pros and cons of trees, pros
-
Disadvantages
-
Ensemble Methods: From a Single Tree to Many Trees and Extreme Boosting, Meet the Jungle of Methods
-
An Overview of Ensemble Methods
-
Bagging
-
More bagging
-
Simple Voting Example, head or tail
-
Using the Voting Classifier
-
Please, not the moons again! Voting and Bagging
-
Bagging Examples
-
Making your own Bootstrap: Changing the Level of the Decision Tree
+
Other ways of visualizing the trees
+
Printing out as text
+
Algorithms for Setting up Decision Trees
+
The CART algorithm for Classification
+
The CART algorithm for Regression
+
Computing the Gini index
+
Simple Python Code to read in Data and perform Classification
+
Computing the Gini Factor
+
Entropy and the ID3 algorithm
+
Cancer Data again now with Decision Trees and other Methods
+
Another example, the moons again
+
Playing around with regions
+
Regression trees
+
Final regressor code
+
Pros and cons of trees, pros
+
Disadvantages
+
Ensemble Methods: From a Single Tree to Many Trees and Extreme Boosting, Meet the Jungle of Methods
+
An Overview of Ensemble Methods
+
Bagging
+
More bagging
+
Simple Voting Example, head or tail
+
Using the Voting Classifier
+
Please, not the moons again! Voting and Bagging
+
Bagging Examples
+
Making your own Bootstrap: Changing the Level of the Decision Tree
@@ -208,54 +212,32 @@ MathJax.Hub.Config({
-
Using the Voting Classifier
+
More bagging
+
+Bagging typically results in improved accuracy
+over prediction using a single tree. Unfortunately, however, it can be
+difficult to interpret the resulting model. Recall that one of the
+advantages of decision trees is the attractive and easily interpreted
+diagram that results.
-
-
from sklearn.model_selection import train_test_split
-from sklearn.datasets import make_moons
+
+However, when we bag a large number of trees, it is no longer
+possible to represent the resulting statistical learning procedure
+using a single tree, and it is no longer clear which variables are
+most important to the procedure. Thus, bagging improves prediction
+accuracy at the expense of interpretability. Although the collection
+of bagged trees is much more difficult to interpret than a single
+tree, one can obtain an overall summary of the importance of each
+predictor using the MSE (for bagging regression trees) or the Gini
+index (for bagging classification trees). In the case of bagging
+regression trees, we can record the total amount that the MSE is
+decreased due to splits over a given predictor, averaged over all \( B \) possible
+trees. A large value indicates an important predictor. Similarly, in
+the context of bagging classification trees, we can add up the total
+amount that the Gini index is decreased by splits over a given
+predictor, averaged over all \( B \) trees.
-X, y = make_moons(n_samples=500 , noise=0.30 , random_state=42 )
-X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42 )
-
-from sklearn.ensemble import RandomForestClassifier
-from sklearn.ensemble import VotingClassifier
-from sklearn.linear_model import LogisticRegression
-from sklearn.svm import SVC
-
-log_clf = LogisticRegression(solver= "liblinear" , random_state=42 )
-rnd_clf = RandomForestClassifier(n_estimators=10 , random_state=42 )
-svm_clf = SVC(gamma= "auto" , random_state=42 )
-
-voting_clf = VotingClassifier(
- estimators= [('lr' , log_clf), ('rf' , rnd_clf), ('svc' , svm_clf)],
- voting= 'hard' )
-
-voting_clf. fit(X_train, y_train)
-
-from sklearn.metrics import accuracy_score
-
-for clf in (log_clf, rnd_clf, svm_clf, voting_clf):
- clf. fit(X_train, y_train)
- y_pred = clf. predict(X_test)
- print (clf. __class__ . __name__ , accuracy_score(y_test, y_pred))
-
-log_clf = LogisticRegression(solver= "liblinear" , random_state=42 )
-rnd_clf = RandomForestClassifier(n_estimators=10 , random_state=42 )
-svm_clf = SVC(gamma= "auto" , probability= True , random_state=42 )
-
-voting_clf = VotingClassifier(
- estimators= [('lr' , log_clf), ('rf' , rnd_clf), ('svc' , svm_clf)],
- voting= 'soft' )
-voting_clf. fit(X_train, y_train)
-
-from sklearn.metrics import accuracy_score
-
-for clf in (log_clf, rnd_clf, svm_clf, voting_clf):
- clf. fit(X_train, y_train)
- y_pred = clf. predict(X_test)
- print (clf. __class__ . __name__ , accuracy_score(y_test, y_pred))
-
@@ -275,6 +257,8 @@ voting_clf. fit(X_train, y_train)
43 44 45
+
46
+
47 »
diff --git a/doc/pub/week44/html/._week44-bs042.html b/doc/pub/week44/html/._week44-bs042.html
index 418a6629a..2782bf6b1 100644
--- a/doc/pub/week44/html/._week44-bs042.html
+++ b/doc/pub/week44/html/._week44-bs042.html
@@ -72,46 +72,48 @@ Automatically generated HTML file from DocOnce source
('Classification tree, how to split nodes', 2, None, '___sec18'),
('Visualizing the Tree, Classification', 2, None, '___sec19'),
('Visualizing the Tree, The Moons', 2, None, '___sec20'),
- ('Algorithms for Setting up Decision Trees', 2, None, '___sec21'),
- ('The CART algorithm for Classification', 2, None, '___sec22'),
- ('The CART algorithm for Regression', 2, None, '___sec23'),
- ('Computing the Gini index', 2, None, '___sec24'),
+ ('Other ways of visualizing the trees', 2, None, '___sec21'),
+ ('Printing out as text', 2, None, '___sec22'),
+ ('Algorithms for Setting up Decision Trees', 2, None, '___sec23'),
+ ('The CART algorithm for Classification', 2, None, '___sec24'),
+ ('The CART algorithm for Regression', 2, None, '___sec25'),
+ ('Computing the Gini index', 2, None, '___sec26'),
('Simple Python Code to read in Data and perform Classification',
2,
None,
- '___sec25'),
- ('Computing the Gini Factor', 2, None, '___sec26'),
- ('Entropy and the ID3 algorithm', 2, None, '___sec27'),
+ '___sec27'),
+ ('Computing the Gini Factor', 2, None, '___sec28'),
+ ('Entropy and the ID3 algorithm', 2, None, '___sec29'),
('Cancer Data again now with Decision Trees and other Methods',
2,
None,
- '___sec28'),
- ('Another example, the moons again', 2, None, '___sec29'),
- ('Playing around with regions', 2, None, '___sec30'),
- ('Regression trees', 2, None, '___sec31'),
- ('Final regressor code', 2, None, '___sec32'),
- ('Pros and cons of trees, pros', 2, None, '___sec33'),
- ('Disadvantages', 2, None, '___sec34'),
+ '___sec30'),
+ ('Another example, the moons again', 2, None, '___sec31'),
+ ('Playing around with regions', 2, None, '___sec32'),
+ ('Regression trees', 2, None, '___sec33'),
+ ('Final regressor code', 2, None, '___sec34'),
+ ('Pros and cons of trees, pros', 2, None, '___sec35'),
+ ('Disadvantages', 2, None, '___sec36'),
('Ensemble Methods: From a Single Tree to Many Trees and Extreme '
'Boosting, Meet the Jungle of Methods',
2,
None,
- '___sec35'),
- ('An Overview of Ensemble Methods', 2, None, '___sec36'),
- ('Bagging', 2, None, '___sec37'),
- ('More bagging', 2, None, '___sec38'),
- ('Simple Voting Example, head or tail', 2, None, '___sec39'),
- ('Using the Voting Classifier', 2, None, '___sec40'),
+ '___sec37'),
+ ('An Overview of Ensemble Methods', 2, None, '___sec38'),
+ ('Bagging', 2, None, '___sec39'),
+ ('More bagging', 2, None, '___sec40'),
+ ('Simple Voting Example, head or tail', 2, None, '___sec41'),
+ ('Using the Voting Classifier', 2, None, '___sec42'),
('Please, not the moons again! Voting and Bagging',
2,
None,
- '___sec41'),
- ('Bagging Examples', 2, None, '___sec42'),
+ '___sec43'),
+ ('Bagging Examples', 2, None, '___sec44'),
('Making your own Bootstrap: Changing the Level of the Decision '
'Tree',
2,
None,
- '___sec43')]}
+ '___sec45')]}
end of tocinfo -->
@@ -170,29 +172,31 @@ MathJax.Hub.Config({
Classification tree, how to split nodes Visualizing the Tree, Classification Visualizing the Tree, The Moons
-
Algorithms for Setting up Decision Trees
-
The CART algorithm for Classification
-
The CART algorithm for Regression
-
Computing the Gini index
-
Simple Python Code to read in Data and perform Classification
-
Computing the Gini Factor
-
Entropy and the ID3 algorithm
-
Cancer Data again now with Decision Trees and other Methods
-
Another example, the moons again
-
Playing around with regions
-
Regression trees
-
Final regressor code
-
Pros and cons of trees, pros
-
Disadvantages
-
Ensemble Methods: From a Single Tree to Many Trees and Extreme Boosting, Meet the Jungle of Methods
-
An Overview of Ensemble Methods
-
Bagging
-
More bagging
-
Simple Voting Example, head or tail
-
Using the Voting Classifier
-
Please, not the moons again! Voting and Bagging
-
Bagging Examples
-
Making your own Bootstrap: Changing the Level of the Decision Tree
+
Other ways of visualizing the trees
+
Printing out as text
+
Algorithms for Setting up Decision Trees
+
The CART algorithm for Classification
+
The CART algorithm for Regression
+
Computing the Gini index
+
Simple Python Code to read in Data and perform Classification
+
Computing the Gini Factor
+
Entropy and the ID3 algorithm
+
Cancer Data again now with Decision Trees and other Methods
+
Another example, the moons again
+
Playing around with regions
+
Regression trees
+
Final regressor code
+
Pros and cons of trees, pros
+
Disadvantages
+
Ensemble Methods: From a Single Tree to Many Trees and Extreme Boosting, Meet the Jungle of Methods
+
An Overview of Ensemble Methods
+
Bagging
+
More bagging
+
Simple Voting Example, head or tail
+
Using the Voting Classifier
+
Please, not the moons again! Voting and Bagging
+
Bagging Examples
+
Making your own Bootstrap: Changing the Level of the Decision Tree
@@ -208,61 +212,23 @@ MathJax.Hub.Config({
-
Please, not the moons again! Voting and Bagging
-
+
Simple Voting Example, head or tail
-
from sklearn.model_selection import train_test_split
-from sklearn.datasets import make_moons
-
-X, y = make_moons(n_samples=500 , noise=0.30 , random_state=42 )
-X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42 )
-from sklearn.ensemble import RandomForestClassifier
-from sklearn.ensemble import VotingClassifier
-from sklearn.linear_model import LogisticRegression
-from sklearn.svm import SVC
-
-log_clf = LogisticRegression(random_state=42 )
-rnd_clf = RandomForestClassifier(random_state=42 )
-svm_clf = SVC(random_state=42 )
-
-voting_clf = VotingClassifier(
- estimators= [('lr' , log_clf), ('rf' , rnd_clf), ('svc' , svm_clf)],
- voting= 'hard' )
-voting_clf. fit(X_train, y_train)
-
-
-
-
-
from sklearn.metrics import accuracy_score
-
-for clf in (log_clf, rnd_clf, svm_clf, voting_clf):
- clf. fit(X_train, y_train)
- y_pred = clf. predict(X_test)
- print (clf. __class__ . __name__ , accuracy_score(y_test, y_pred))
-
-
-
-
-
= LogisticRegression(random_state=42 )
-rnd_clf = RandomForestClassifier(random_state=42 )
-svm_clf = SVC(probability= True , random_state=42 )
-
-voting_clf = VotingClassifier(
- estimators= [('lr' , log_clf), ('rf' , rnd_clf), ('svc' , svm_clf)],
- voting= 'soft' )
-voting_clf. fit(X_train, y_train)
-
-
-
-
-
from sklearn.metrics import accuracy_score
-
-for clf in (log_clf, rnd_clf, svm_clf, voting_clf):
- clf. fit(X_train, y_train)
- y_pred = clf. predict(X_test)
- print (clf. __class__ . __name__ , accuracy_score(y_test, y_pred))
+= 0.51
+coin_tosses = (np. random. rand(10000 , 10 ) < heads_proba). astype(np. int32)
+cumulative_heads_ratio = np. cumsum(coin_tosses, axis=0 ) / np. arange(1 , 10001 ). reshape(-1 , 1 )
+plt. figure(figsize= (8 ,3.5 ))
+plt. plot(cumulative_heads_ratio)
+plt. plot([0 , 10000 ], [0.51 , 0.51 ], "k--" , linewidth=2 , label= "51%" )
+plt. plot([0 , 10000 ], [0.5 , 0.5 ], "k-" , label= "50%" )
+plt. xlabel("Number of coin tosses" )
+plt. ylabel("Heads ratio" )
+plt. legend(loc= "lower right" )
+plt. axis([0 , 10000 , 0.42 , 0.58 ])
+save_fig("votingsimple" )
+plt. show()
@@ -282,6 +248,8 @@ voting_clf. fit(X_train, y_train)
43 44 45
+
46
+
47 »
diff --git a/doc/pub/week44/html/week44-bs.html b/doc/pub/week44/html/week44-bs.html
index f9e5bfd65..2030cd05f 100644
--- a/doc/pub/week44/html/week44-bs.html
+++ b/doc/pub/week44/html/week44-bs.html
@@ -72,46 +72,48 @@ Automatically generated HTML file from DocOnce source
('Classification tree, how to split nodes', 2, None, '___sec18'),
('Visualizing the Tree, Classification', 2, None, '___sec19'),
('Visualizing the Tree, The Moons', 2, None, '___sec20'),
- ('Algorithms for Setting up Decision Trees', 2, None, '___sec21'),
- ('The CART algorithm for Classification', 2, None, '___sec22'),
- ('The CART algorithm for Regression', 2, None, '___sec23'),
- ('Computing the Gini index', 2, None, '___sec24'),
+ ('Other ways of visualizing the trees', 2, None, '___sec21'),
+ ('Printing out as text', 2, None, '___sec22'),
+ ('Algorithms for Setting up Decision Trees', 2, None, '___sec23'),
+ ('The CART algorithm for Classification', 2, None, '___sec24'),
+ ('The CART algorithm for Regression', 2, None, '___sec25'),
+ ('Computing the Gini index', 2, None, '___sec26'),
('Simple Python Code to read in Data and perform Classification',
2,
None,
- '___sec25'),
- ('Computing the Gini Factor', 2, None, '___sec26'),
- ('Entropy and the ID3 algorithm', 2, None, '___sec27'),
+ '___sec27'),
+ ('Computing the Gini Factor', 2, None, '___sec28'),
+ ('Entropy and the ID3 algorithm', 2, None, '___sec29'),
('Cancer Data again now with Decision Trees and other Methods',
2,
None,
- '___sec28'),
- ('Another example, the moons again', 2, None, '___sec29'),
- ('Playing around with regions', 2, None, '___sec30'),
- ('Regression trees', 2, None, '___sec31'),
- ('Final regressor code', 2, None, '___sec32'),
- ('Pros and cons of trees, pros', 2, None, '___sec33'),
- ('Disadvantages', 2, None, '___sec34'),
+ '___sec30'),
+ ('Another example, the moons again', 2, None, '___sec31'),
+ ('Playing around with regions', 2, None, '___sec32'),
+ ('Regression trees', 2, None, '___sec33'),
+ ('Final regressor code', 2, None, '___sec34'),
+ ('Pros and cons of trees, pros', 2, None, '___sec35'),
+ ('Disadvantages', 2, None, '___sec36'),
('Ensemble Methods: From a Single Tree to Many Trees and Extreme '
'Boosting, Meet the Jungle of Methods',
2,
None,
- '___sec35'),
- ('An Overview of Ensemble Methods', 2, None, '___sec36'),
- ('Bagging', 2, None, '___sec37'),
- ('More bagging', 2, None, '___sec38'),
- ('Simple Voting Example, head or tail', 2, None, '___sec39'),
- ('Using the Voting Classifier', 2, None, '___sec40'),
+ '___sec37'),
+ ('An Overview of Ensemble Methods', 2, None, '___sec38'),
+ ('Bagging', 2, None, '___sec39'),
+ ('More bagging', 2, None, '___sec40'),
+ ('Simple Voting Example, head or tail', 2, None, '___sec41'),
+ ('Using the Voting Classifier', 2, None, '___sec42'),
('Please, not the moons again! Voting and Bagging',
2,
None,
- '___sec41'),
- ('Bagging Examples', 2, None, '___sec42'),
+ '___sec43'),
+ ('Bagging Examples', 2, None, '___sec44'),
('Making your own Bootstrap: Changing the Level of the Decision '
'Tree',
2,
None,
- '___sec43')]}
+ '___sec45')]}
end of tocinfo -->
@@ -170,29 +172,31 @@ MathJax.Hub.Config({
Classification tree, how to split nodes Visualizing the Tree, Classification Visualizing the Tree, The Moons
-
Algorithms for Setting up Decision Trees
-
The CART algorithm for Classification
-
The CART algorithm for Regression
-
Computing the Gini index
-
Simple Python Code to read in Data and perform Classification
-
Computing the Gini Factor
-
Entropy and the ID3 algorithm
-
Cancer Data again now with Decision Trees and other Methods
-
Another example, the moons again
-
Playing around with regions
-
Regression trees
-
Final regressor code
-
Pros and cons of trees, pros
-
Disadvantages
-
Ensemble Methods: From a Single Tree to Many Trees and Extreme Boosting, Meet the Jungle of Methods
-
An Overview of Ensemble Methods
-
Bagging
-
More bagging
-
Simple Voting Example, head or tail
-
Using the Voting Classifier
-
Please, not the moons again! Voting and Bagging
-
Bagging Examples
-
Making your own Bootstrap: Changing the Level of the Decision Tree
+
Other ways of visualizing the trees
+
Printing out as text
+
Algorithms for Setting up Decision Trees
+
The CART algorithm for Classification
+
The CART algorithm for Regression
+
Computing the Gini index
+
Simple Python Code to read in Data and perform Classification
+
Computing the Gini Factor
+
Entropy and the ID3 algorithm
+
Cancer Data again now with Decision Trees and other Methods
+
Another example, the moons again
+
Playing around with regions
+
Regression trees
+
Final regressor code
+
Pros and cons of trees, pros
+
Disadvantages
+
Ensemble Methods: From a Single Tree to Many Trees and Extreme Boosting, Meet the Jungle of Methods
+
An Overview of Ensemble Methods
+
Bagging
+
More bagging
+
Simple Voting Example, head or tail
+
Using the Voting Classifier
+
Please, not the moons again! Voting and Bagging
+
Bagging Examples
+
Making your own Bootstrap: Changing the Level of the Decision Tree
@@ -227,7 +231,7 @@ MathJax.Hub.Config({
[2] Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University
-
Oct 27, 2020
+
Oct 30, 2020
@@ -251,7 +255,7 @@ MathJax.Hub.Config({
9 10 ...
-
45
+
47 »
diff --git a/doc/pub/week44/html/week44-reveal.html b/doc/pub/week44/html/week44-reveal.html
index 52b9c8aac..f4e881651 100644
--- a/doc/pub/week44/html/week44-reveal.html
+++ b/doc/pub/week44/html/week44-reveal.html
@@ -148,7 +148,7 @@ MathJax.Hub.Config({
[2] Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University
Oct 27, 2020
+
Oct 30, 2020
@@ -162,7 +162,7 @@ MathJax.Hub.Config({
Overview of week 44
@@ -173,9 +173,6 @@ Geron's chapter 6 covers decision trees while ensemble models, voting and baggin
Thursday
-
-
-Overview video, aims and motivations.
@@ -512,11 +509,16 @@ The so-called Cost complexity pruning algorithm gives us a
way to do just this. Rather than considering every possible subtree,
we consider a sequence of trees indexed by a nonnegative tuning
parameter \( \alpha \).
+
+
+Read more at the following Scikit-Learn link on pruning .
Cost complexity pruning
+
+
For each value of \( \alpha \) there corresponds a subtree \( T \in T_0 \) such that
The tuning parameter \( \alpha \) controls a trade-off between the subtree’s
-com- plexity and its fit to the training data. When \( \alpha = 0 \), then the
+complexity and its fit to the training data. When \( \alpha = 0 \), then the
subtree \( T \) will simply equal \( T_0 \),
because then the above equation just measures the
training error.
@@ -747,7 +749,49 @@ os.system(cmd)
-Algorithms for Setting up Decision Trees
+Other ways of visualizing the trees
+
+
+Scikit-Learn has also another way to visualize the trees which is very useful, here with the Iris data.
+
+
+
+
+
from sklearn.datasets import load_iris
+from sklearn import tree
+X, y = load_iris(return_X_y=True )
+tree_clf = tree.DecisionTreeClassifier()
+tree_clf = tree_clf.fit(X, y)
+# and then plot the tree
+tree.plot_tree(tree_clf)
+
+
+
+
+Printing out as text
+
+
+Alternatively, the tree can also be exported in textual format with the function exporttext.
+This method doesn’t require the installation of external libraries and is more compact:
+
+
+
+
+
from sklearn.datasets import load_iris
+from sklearn.tree import DecisionTreeClassifier
+from sklearn.tree import export_text
+iris = load_iris()
+decision_tree = DecisionTreeClassifier(random_state=0 , max_depth=2 )
+decision_tree = decision_tree.fit(iris.data, iris.target)
+r = export_text(decision_tree, feature_names=iris['feature_names' ])
+print (r)
+
+
+
+
+Algorithms for Setting up Decision Trees
Two algorithms stand out in the set up of decision trees:
@@ -766,7 +810,7 @@ in two branches.
-The CART algorithm for Classification
+The CART algorithm for Classification
For classification, the CART algorithm splits the data set in two subsets using a single feature \( k \) and a threshold \( t_k \).
@@ -795,7 +839,7 @@ hyperparameters control additional stopping conditions such as the \( min\_sampl
-The CART algorithm for Regression
+The CART algorithm for Regression
The CART algorithm for regression works is similar to the one for classification except that instead of trying to split the
@@ -829,7 +873,7 @@ just like for classification tasks, is prone to overfitting.
-Computing the Gini index
+Computing the Gini index
The example we will look at is a classical one in many Machine
@@ -870,7 +914,7 @@ The table here summarizes the various attributes and
-Simple Python Code to read in Data and perform Classification
+Simple Python Code to read in Data and perform Classification
@@ -947,7 +991,7 @@ os.system(cmd)
-Computing the Gini Factor
+Computing the Gini Factor
The above functions (gini, entropy and misclassification error) are
@@ -1026,7 +1070,7 @@ split = get_split(dataset)
-Entropy and the ID3 algorithm
+Entropy and the ID3 algorithm
The ID3 algorithm learns decision trees by constructing
@@ -1063,7 +1107,7 @@ attributes at each step while growing the tree.
-Cancer Data again now with Decision Trees and other Methods
+Cancer Data again now with Decision Trees and other Methods
@@ -1113,7 +1157,7 @@ deep_tree_clf.fit(X_train_scaled, y_train)
-Another example, the moons again
+Another example, the moons again
@@ -1186,7 +1230,7 @@ plt.show()
-Playing around with regions
+Playing around with regions
@@ -1215,7 +1259,7 @@ plt.show()
-Regression trees
+Regression trees
@@ -1238,7 +1282,7 @@ tree_reg.fit(X, y)
-Final regressor code
+Final regressor code
@@ -1317,7 +1361,7 @@ plt.show()
-Pros and cons of trees, pros
+Pros and cons of trees, pros
White box, easy to interpret model. Some people believe that decision trees more closely mirror human decision-making than do the regression and classification approaches discussed earlier (think of support vector machines)
@@ -1332,7 +1376,7 @@ plt.show()
-Disadvantages
+Disadvantages
Unfortunately, trees generally do not have the same level of predictive accuracy as some of the other regression and classification approaches
@@ -1352,7 +1396,7 @@ trees can be substantially improved.
-Ensemble Methods: From a Single Tree to Many Trees and Extreme Boosting, Meet the Jungle of Methods
+Ensemble Methods: From a Single Tree to Many Trees and Extreme Boosting, Meet the Jungle of Methods
As stated above and seen in many of the examples discussed here about
@@ -1380,7 +1424,7 @@ We discuss these methods here.
-An Overview of Ensemble Methods
+An Overview of Ensemble Methods
-Bagging
+Bagging
The plain decision trees suffer from high
@@ -1407,7 +1451,7 @@ learning method.
-More bagging
+More bagging
Bagging typically results in improved accuracy
@@ -1436,7 +1480,7 @@ predictor, averaged over all \( B \) trees.
-Simple Voting Example, head or tail
+Simple Voting Example, head or tail
@@ -1458,7 +1502,7 @@ plt.show()
-Using the Voting Classifier
+Using the Voting Classifier
@@ -1510,7 +1554,7 @@ voting_clf.fit(X_train, y_train)
-Please, not the moons again! Voting and Bagging
+Please, not the moons again! Voting and Bagging
@@ -1570,7 +1614,7 @@ voting_clf.fit(X_train, y_train)
-Bagging Examples
+Bagging Examples
@@ -1633,7 +1677,7 @@ plt.show()
-Making your own Bootstrap: Changing the Level of the Decision Tree
+Making your own Bootstrap: Changing the Level of the Decision Tree
Let us bring up our good old boostrap example from the linear regression lectures. We change the linerar regression algorithm with
diff --git a/doc/pub/week44/html/week44-solarized.html b/doc/pub/week44/html/week44-solarized.html
index a765beb09..186fcc37b 100644
--- a/doc/pub/week44/html/week44-solarized.html
+++ b/doc/pub/week44/html/week44-solarized.html
@@ -92,46 +92,48 @@ div { text-align: justify; text-justify: inter-word; }
('Classification tree, how to split nodes', 2, None, '___sec18'),
('Visualizing the Tree, Classification', 2, None, '___sec19'),
('Visualizing the Tree, The Moons', 2, None, '___sec20'),
- ('Algorithms for Setting up Decision Trees', 2, None, '___sec21'),
- ('The CART algorithm for Classification', 2, None, '___sec22'),
- ('The CART algorithm for Regression', 2, None, '___sec23'),
- ('Computing the Gini index', 2, None, '___sec24'),
+ ('Other ways of visualizing the trees', 2, None, '___sec21'),
+ ('Printing out as text', 2, None, '___sec22'),
+ ('Algorithms for Setting up Decision Trees', 2, None, '___sec23'),
+ ('The CART algorithm for Classification', 2, None, '___sec24'),
+ ('The CART algorithm for Regression', 2, None, '___sec25'),
+ ('Computing the Gini index', 2, None, '___sec26'),
('Simple Python Code to read in Data and perform Classification',
2,
None,
- '___sec25'),
- ('Computing the Gini Factor', 2, None, '___sec26'),
- ('Entropy and the ID3 algorithm', 2, None, '___sec27'),
+ '___sec27'),
+ ('Computing the Gini Factor', 2, None, '___sec28'),
+ ('Entropy and the ID3 algorithm', 2, None, '___sec29'),
('Cancer Data again now with Decision Trees and other Methods',
2,
None,
- '___sec28'),
- ('Another example, the moons again', 2, None, '___sec29'),
- ('Playing around with regions', 2, None, '___sec30'),
- ('Regression trees', 2, None, '___sec31'),
- ('Final regressor code', 2, None, '___sec32'),
- ('Pros and cons of trees, pros', 2, None, '___sec33'),
- ('Disadvantages', 2, None, '___sec34'),
+ '___sec30'),
+ ('Another example, the moons again', 2, None, '___sec31'),
+ ('Playing around with regions', 2, None, '___sec32'),
+ ('Regression trees', 2, None, '___sec33'),
+ ('Final regressor code', 2, None, '___sec34'),
+ ('Pros and cons of trees, pros', 2, None, '___sec35'),
+ ('Disadvantages', 2, None, '___sec36'),
('Ensemble Methods: From a Single Tree to Many Trees and Extreme '
'Boosting, Meet the Jungle of Methods',
2,
None,
- '___sec35'),
- ('An Overview of Ensemble Methods', 2, None, '___sec36'),
- ('Bagging', 2, None, '___sec37'),
- ('More bagging', 2, None, '___sec38'),
- ('Simple Voting Example, head or tail', 2, None, '___sec39'),
- ('Using the Voting Classifier', 2, None, '___sec40'),
+ '___sec37'),
+ ('An Overview of Ensemble Methods', 2, None, '___sec38'),
+ ('Bagging', 2, None, '___sec39'),
+ ('More bagging', 2, None, '___sec40'),
+ ('Simple Voting Example, head or tail', 2, None, '___sec41'),
+ ('Using the Voting Classifier', 2, None, '___sec42'),
('Please, not the moons again! Voting and Bagging',
2,
None,
- '___sec41'),
- ('Bagging Examples', 2, None, '___sec42'),
+ '___sec43'),
+ ('Bagging Examples', 2, None, '___sec44'),
('Making your own Bootstrap: Changing the Level of the Decision '
'Tree',
2,
None,
- '___sec43')]}
+ '___sec45')]}
end of tocinfo -->
@@ -173,7 +175,7 @@ MathJax.Hub.Config({
[2] Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University
-
Oct 27, 2020 Oct 30, 2020
Overview of week 44
@@ -192,9 +194,6 @@ Geron's chapter 6 covers decision trees while ensemble models, voting and baggin
Thursday
-
-Overview video, aims and motivations.
-
+Read more at the following Scikit-Learn link on pruning .
+
Cost complexity pruning
+
+
For each value of \( \alpha \) there corresponds a subtree \( T \in T_0 \) such that
$$
\sum_{m=1}^{\overline{T}}\sum_{i:x_i\in R_m}(y_i-\overline{y}_{R_m})^2+\alpha\overline{T},
@@ -533,7 +537,7 @@ rectangle (i.e. the subset of predictor space) corresponding to the \( m \)-th
The tuning parameter \( \alpha \) controls a trade-off between the subtree’s
-com- plexity and its fit to the training data. When \( \alpha = 0 \), then the
+complexity and its fit to the training data. When \( \alpha = 0 \), then the
subtree \( T \) will simply equal \( T_0 \),
because then the above equation just measures the
training error.
@@ -744,7 +748,47 @@ os.system(cmd)
Algorithms for Setting up Decision Trees
+Other ways of visualizing the trees
+
+
+Scikit-Learn has also another way to visualize the trees which is very useful, here with the Iris data.
+
+
+
+
+
from sklearn.datasets import load_iris
+from sklearn import tree
+X, y = load_iris(return_X_y=True )
+tree_clf = tree.DecisionTreeClassifier()
+tree_clf = tree_clf.fit(X, y)
+# and then plot the tree
+tree.plot_tree(tree_clf)
+
+
Printing out as text
+
+
+Alternatively, the tree can also be exported in textual format with the function exporttext.
+This method doesn’t require the installation of external libraries and is more compact:
+
+
+
+
+
from sklearn.datasets import load_iris
+from sklearn.tree import DecisionTreeClassifier
+from sklearn.tree import export_text
+iris = load_iris()
+decision_tree = DecisionTreeClassifier(random_state=0 , max_depth=2 )
+decision_tree = decision_tree.fit(iris.data, iris.target)
+r = export_text(decision_tree, feature_names=iris['feature_names' ])
+print (r)
+
+
Algorithms for Setting up Decision Trees
Two algorithms stand out in the set up of decision trees:
@@ -762,7 +806,7 @@ in two branches.
The CART algorithm for Classification
+The CART algorithm for Classification
For classification, the CART algorithm splits the data set in two subsets using a single feature \( k \) and a threshold \( t_k \).
@@ -789,7 +833,7 @@ hyperparameters control additional stopping conditions such as the \( min\_sampl
The CART algorithm for Regression
+The CART algorithm for Regression
The CART algorithm for regression works is similar to the one for classification except that instead of trying to split the
@@ -817,7 +861,7 @@ just like for classification tasks, is prone to overfitting.
Computing the Gini index
+Computing the Gini index
The example we will look at is a classical one in many Machine
@@ -857,7 +901,7 @@ The table here summarizes the various attributes and
Simple Python Code to read in Data and perform Classification
+Simple Python Code to read in Data and perform Classification
@@ -933,7 +977,7 @@ os.system(cmd)
Computing the Gini Factor
+Computing the Gini Factor
The above functions (gini, entropy and misclassification error) are
@@ -1011,7 +1055,7 @@ split = get_split(dataset)
Entropy and the ID3 algorithm
+Entropy and the ID3 algorithm
The ID3 algorithm learns decision trees by constructing
@@ -1047,7 +1091,7 @@ attributes at each step while growing the tree.
Cancer Data again now with Decision Trees and other Methods
+Cancer Data again now with Decision Trees and other Methods
@@ -1096,7 +1140,7 @@ deep_tree_clf.fit(X_train_scaled, y_train)
Another example, the moons again
+Another example, the moons again
@@ -1168,7 +1212,7 @@ plt.show()
Playing around with regions
+Playing around with regions
@@ -1196,7 +1240,7 @@ plt.show()
Regression trees
+Regression trees
@@ -1218,7 +1262,7 @@ tree_reg.fit(X, y)
Final regressor code
+Final regressor code
@@ -1296,7 +1340,7 @@ plt.show()
Pros and cons of trees, pros
+Pros and cons of trees, pros
White box, easy to interpret model. Some people believe that decision trees more closely mirror human decision-making than do the regression and classification approaches discussed earlier (think of support vector machines)
@@ -1310,7 +1354,7 @@ plt.show()
Disadvantages
+Disadvantages
Unfortunately, trees generally do not have the same level of predictive accuracy as some of the other regression and classification approaches
@@ -1329,7 +1373,7 @@ trees can be substantially improved.
Ensemble Methods: From a Single Tree to Many Trees and Extreme Boosting, Meet the Jungle of Methods
+Ensemble Methods: From a Single Tree to Many Trees and Extreme Boosting, Meet the Jungle of Methods
As stated above and seen in many of the examples discussed here about
@@ -1356,7 +1400,7 @@ We discuss these methods here.
An Overview of Ensemble Methods
+An Overview of Ensemble Methods
Bagging
+Bagging
The plain decision trees suffer from high
@@ -1383,7 +1427,7 @@ learning method.
More bagging
+More bagging
Bagging typically results in improved accuracy
@@ -1412,7 +1456,7 @@ predictor, averaged over all \( B \) trees.
Simple Voting Example, head or tail
+Simple Voting Example, head or tail
@@ -1433,7 +1477,7 @@ plt.show()
Using the Voting Classifier
+Using the Voting Classifier
@@ -1484,7 +1528,7 @@ voting_clf.fit(X_train, y_train)
Please, not the moons again! Voting and Bagging
+Please, not the moons again! Voting and Bagging
@@ -1543,7 +1587,7 @@ voting_clf.fit(X_train, y_train)
Bagging Examples
+Bagging Examples
@@ -1605,7 +1649,7 @@ plt.show()
Making your own Bootstrap: Changing the Level of the Decision Tree
+Making your own Bootstrap: Changing the Level of the Decision Tree
Let us bring up our good old boostrap example from the linear regression lectures. We change the linerar regression algorithm with
diff --git a/doc/pub/week44/html/week44.html b/doc/pub/week44/html/week44.html
index 6b4da8ab4..f4052ab45 100644
--- a/doc/pub/week44/html/week44.html
+++ b/doc/pub/week44/html/week44.html
@@ -97,46 +97,48 @@ div { text-align: justify; text-justify: inter-word; }
('Classification tree, how to split nodes', 2, None, '___sec18'),
('Visualizing the Tree, Classification', 2, None, '___sec19'),
('Visualizing the Tree, The Moons', 2, None, '___sec20'),
- ('Algorithms for Setting up Decision Trees', 2, None, '___sec21'),
- ('The CART algorithm for Classification', 2, None, '___sec22'),
- ('The CART algorithm for Regression', 2, None, '___sec23'),
- ('Computing the Gini index', 2, None, '___sec24'),
+ ('Other ways of visualizing the trees', 2, None, '___sec21'),
+ ('Printing out as text', 2, None, '___sec22'),
+ ('Algorithms for Setting up Decision Trees', 2, None, '___sec23'),
+ ('The CART algorithm for Classification', 2, None, '___sec24'),
+ ('The CART algorithm for Regression', 2, None, '___sec25'),
+ ('Computing the Gini index', 2, None, '___sec26'),
('Simple Python Code to read in Data and perform Classification',
2,
None,
- '___sec25'),
- ('Computing the Gini Factor', 2, None, '___sec26'),
- ('Entropy and the ID3 algorithm', 2, None, '___sec27'),
+ '___sec27'),
+ ('Computing the Gini Factor', 2, None, '___sec28'),
+ ('Entropy and the ID3 algorithm', 2, None, '___sec29'),
('Cancer Data again now with Decision Trees and other Methods',
2,
None,
- '___sec28'),
- ('Another example, the moons again', 2, None, '___sec29'),
- ('Playing around with regions', 2, None, '___sec30'),
- ('Regression trees', 2, None, '___sec31'),
- ('Final regressor code', 2, None, '___sec32'),
- ('Pros and cons of trees, pros', 2, None, '___sec33'),
- ('Disadvantages', 2, None, '___sec34'),
+ '___sec30'),
+ ('Another example, the moons again', 2, None, '___sec31'),
+ ('Playing around with regions', 2, None, '___sec32'),
+ ('Regression trees', 2, None, '___sec33'),
+ ('Final regressor code', 2, None, '___sec34'),
+ ('Pros and cons of trees, pros', 2, None, '___sec35'),
+ ('Disadvantages', 2, None, '___sec36'),
('Ensemble Methods: From a Single Tree to Many Trees and Extreme '
'Boosting, Meet the Jungle of Methods',
2,
None,
- '___sec35'),
- ('An Overview of Ensemble Methods', 2, None, '___sec36'),
- ('Bagging', 2, None, '___sec37'),
- ('More bagging', 2, None, '___sec38'),
- ('Simple Voting Example, head or tail', 2, None, '___sec39'),
- ('Using the Voting Classifier', 2, None, '___sec40'),
+ '___sec37'),
+ ('An Overview of Ensemble Methods', 2, None, '___sec38'),
+ ('Bagging', 2, None, '___sec39'),
+ ('More bagging', 2, None, '___sec40'),
+ ('Simple Voting Example, head or tail', 2, None, '___sec41'),
+ ('Using the Voting Classifier', 2, None, '___sec42'),
('Please, not the moons again! Voting and Bagging',
2,
None,
- '___sec41'),
- ('Bagging Examples', 2, None, '___sec42'),
+ '___sec43'),
+ ('Bagging Examples', 2, None, '___sec44'),
('Making your own Bootstrap: Changing the Level of the Decision '
'Tree',
2,
None,
- '___sec43')]}
+ '___sec45')]}
end of tocinfo -->
@@ -178,7 +180,7 @@ MathJax.Hub.Config({
[2] Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University
-
Oct 27, 2020 Oct 30, 2020
Overview of week 44
@@ -197,9 +199,6 @@ Geron's chapter 6 covers decision trees while ensemble models, voting and baggin
Thursday
-
-Overview video, aims and motivations.
-
+Read more at the following Scikit-Learn link on pruning .
+
Cost complexity pruning
+
+
For each value of \( \alpha \) there corresponds a subtree \( T \in T_0 \) such that
$$
\sum_{m=1}^{\overline{T}}\sum_{i:x_i\in R_m}(y_i-\overline{y}_{R_m})^2+\alpha\overline{T},
@@ -538,7 +542,7 @@ rectangle (i.e. the subset of predictor space) corresponding to the \( m \)-th
The tuning parameter \( \alpha \) controls a trade-off between the subtree’s
-com- plexity and its fit to the training data. When \( \alpha = 0 \), then the
+complexity and its fit to the training data. When \( \alpha = 0 \), then the
subtree \( T \) will simply equal \( T_0 \),
because then the above equation just measures the
training error.
@@ -749,7 +753,47 @@ os. system(cmd)
Algorithms for Setting up Decision Trees
+Other ways of visualizing the trees
+
+
+Scikit-Learn has also another way to visualize the trees which is very useful, here with the Iris data.
+
+
+
+
+
from sklearn.datasets import load_iris
+from sklearn import tree
+X, y = load_iris(return_X_y= True )
+tree_clf = tree. DecisionTreeClassifier()
+tree_clf = tree_clf. fit(X, y)
+# and then plot the tree
+tree. plot_tree(tree_clf)
+
+
Printing out as text
+
+
+Alternatively, the tree can also be exported in textual format with the function exporttext.
+This method doesn’t require the installation of external libraries and is more compact:
+
+
+
+
+
from sklearn.datasets import load_iris
+from sklearn.tree import DecisionTreeClassifier
+from sklearn.tree import export_text
+iris = load_iris()
+decision_tree = DecisionTreeClassifier(random_state=0 , max_depth=2 )
+decision_tree = decision_tree. fit(iris. data, iris. target)
+r = export_text(decision_tree, feature_names= iris['feature_names' ])
+print (r)
+
+
Algorithms for Setting up Decision Trees
Two algorithms stand out in the set up of decision trees:
@@ -767,7 +811,7 @@ in two branches.
The CART algorithm for Classification
+The CART algorithm for Classification
For classification, the CART algorithm splits the data set in two subsets using a single feature \( k \) and a threshold \( t_k \).
@@ -794,7 +838,7 @@ hyperparameters control additional stopping conditions such as the \( min\_sampl
The CART algorithm for Regression
+The CART algorithm for Regression
The CART algorithm for regression works is similar to the one for classification except that instead of trying to split the
@@ -822,7 +866,7 @@ just like for classification tasks, is prone to overfitting.
Computing the Gini index
+Computing the Gini index
The example we will look at is a classical one in many Machine
@@ -862,7 +906,7 @@ The table here summarizes the various attributes and
Simple Python Code to read in Data and perform Classification
+Simple Python Code to read in Data and perform Classification
@@ -938,7 +982,7 @@ os. system(cmd)
Computing the Gini Factor
+Computing the Gini Factor
The above functions (gini, entropy and misclassification error) are
@@ -1016,7 +1060,7 @@ split = get_split(dataset)
Entropy and the ID3 algorithm
+Entropy and the ID3 algorithm
The ID3 algorithm learns decision trees by constructing
@@ -1052,7 +1096,7 @@ attributes at each step while growing the tree.
Cancer Data again now with Decision Trees and other Methods
+Cancer Data again now with Decision Trees and other Methods
@@ -1101,7 +1145,7 @@ deep_tree_clf. fit(X_train_scaled, y_train)
Another example, the moons again
+Another example, the moons again
@@ -1173,7 +1217,7 @@ plt. show()
Playing around with regions
+Playing around with regions
@@ -1201,7 +1245,7 @@ plt. show()
Regression trees
+Regression trees
@@ -1223,7 +1267,7 @@ tree_reg. fit(X, y)
Final regressor code
+Final regressor code
@@ -1301,7 +1345,7 @@ plt. show()
Pros and cons of trees, pros
+Pros and cons of trees, pros
White box, easy to interpret model. Some people believe that decision trees more closely mirror human decision-making than do the regression and classification approaches discussed earlier (think of support vector machines)
@@ -1315,7 +1359,7 @@ plt. show()
Disadvantages
+Disadvantages
Unfortunately, trees generally do not have the same level of predictive accuracy as some of the other regression and classification approaches
@@ -1334,7 +1378,7 @@ trees can be substantially improved.
Ensemble Methods: From a Single Tree to Many Trees and Extreme Boosting, Meet the Jungle of Methods
+Ensemble Methods: From a Single Tree to Many Trees and Extreme Boosting, Meet the Jungle of Methods
As stated above and seen in many of the examples discussed here about
@@ -1361,7 +1405,7 @@ We discuss these methods here.
An Overview of Ensemble Methods
+An Overview of Ensemble Methods
Bagging
+Bagging
The plain decision trees suffer from high
@@ -1388,7 +1432,7 @@ learning method.
More bagging
+More bagging
Bagging typically results in improved accuracy
@@ -1417,7 +1461,7 @@ predictor, averaged over all \( B \) trees.
Simple Voting Example, head or tail
+Simple Voting Example, head or tail
@@ -1438,7 +1482,7 @@ plt. show()
Using the Voting Classifier
+Using the Voting Classifier
@@ -1489,7 +1533,7 @@ voting_clf. fit(X_train, y_train)
Please, not the moons again! Voting and Bagging
+Please, not the moons again! Voting and Bagging
@@ -1548,7 +1592,7 @@ voting_clf. fit(X_train, y_train)
Bagging Examples
+Bagging Examples
@@ -1610,7 +1654,7 @@ plt. show()
Making your own Bootstrap: Changing the Level of the Decision Tree
+Making your own Bootstrap: Changing the Level of the Decision Tree
Let us bring up our good old boostrap example from the linear regression lectures. We change the linerar regression algorithm with
diff --git a/doc/pub/week44/ipynb/ipynb-week44-src.tar.gz b/doc/pub/week44/ipynb/ipynb-week44-src.tar.gz
index 8275e84b8..81308e3b6 100644
Binary files a/doc/pub/week44/ipynb/ipynb-week44-src.tar.gz and b/doc/pub/week44/ipynb/ipynb-week44-src.tar.gz differ
diff --git a/doc/pub/week44/ipynb/week44.ipynb b/doc/pub/week44/ipynb/week44.ipynb
index 917256cc0..ed722846a 100644
--- a/doc/pub/week44/ipynb/week44.ipynb
+++ b/doc/pub/week44/ipynb/week44.ipynb
@@ -10,7 +10,7 @@
" \n",
"**Morten Hjorth-Jensen**, Department of Physics, University of Oslo and Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University\n",
"\n",
- "Date: **Oct 27, 2020**\n",
+ "Date: **Oct 30, 2020**\n",
"\n",
"Copyright 1999-2020, Morten Hjorth-Jensen. Released under CC Attribution-NonCommercial 4.0 license\n",
"\n",
@@ -19,7 +19,7 @@
"\n",
"## Overview of week 44\n",
"\n",
- "* Thursday: Wrapping up PCA from last week and basics of decision trees, classification and regression algorithms \n",
+ "* [Thursday: Wrapping up PCA from last week and basics of decision trees, classification and regression algorithms with video of lecture](https://www.uio.no/studier/emner/matnat/fys/FYS-STK4155/h20/forelesningsvideoer/LectureOctober29.mp4?vrtx=view-as-webpage) \n",
"\n",
"* Friday: Decision trees, voting models and bagging\n",
"\n",
@@ -28,7 +28,7 @@
"\n",
"## Thursday\n",
"\n",
- "Overview video, aims and motivations. \n",
+ "\n",
"\n",
"## Decision trees, overarching aims\n",
"\n",
@@ -135,44 +135,11 @@
},
{
"cell_type": "code",
- "execution_count": 24,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "2nd degree coefficients:\n",
- "zero power: -2.188618732996508\n",
- "first power: 0.09515893290237344\n",
- "second power: -0.0004760754513086032\n"
- ]
- },
- {
- "data": {
- "image/png": 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\n",
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- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- },
- {
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\n",
- "text/plain": [
- ""
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
+ "execution_count": 1,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
"source": [
"%matplotlib inline\n",
"\n",
@@ -241,7 +208,7 @@
"from sklearn.tree import DecisionTreeRegressor\n",
"regr_1=DecisionTreeRegressor(max_depth=2)\n",
"regr_2=DecisionTreeRegressor(max_depth=5)\n",
- "regr_3=DecisionTreeRegressor(max_depth=11)\n",
+ "regr_3=DecisionTreeRegressor(max_depth=7)\n",
"regr_1.fit(X, distance_list)\n",
"regr_2.fit(X, distance_list)\n",
"regr_3.fit(X, distance_list)\n",
@@ -406,7 +373,10 @@
"we consider a sequence of trees indexed by a nonnegative tuning\n",
"parameter $\\alpha$.\n",
"\n",
+ "Read more at the following [Scikit-Learn link on pruning](https://scikit-learn.org/stable/auto_examples/tree/plot_cost_complexity_pruning.html#sphx-glr-auto-examples-tree-plot-cost-complexity-pruning-py).\n",
+ "\n",
"## Cost complexity pruning\n",
+ "\n",
"For each value of $\\alpha$ there corresponds a subtree $T \\in T_0$ such that"
]
},
@@ -428,7 +398,7 @@
"rectangle (i.e. the subset of predictor space) corresponding to the $m$-th terminal node.\n",
"\n",
"The tuning parameter $\\alpha$ controls a trade-off between the subtree’s\n",
- "com- plexity and its fit to the training data. When $\\alpha = 0$, then the\n",
+ "complexity and its fit to the training data. When $\\alpha = 0$, then the\n",
"subtree $T$ will simply equal $T_0$, \n",
"because then the above equation just measures the\n",
"training error. \n",
@@ -449,6 +419,7 @@
"\n",
"**Building a Regression Tree.**\n",
"\n",
+ "\n",
"1. Use recursive binary splitting to grow a large tree on the training data, stopping only when each terminal node has fewer than some minimum number of observations.\n",
"\n",
"2. Apply cost complexity pruning to the large tree in order to obtain a sequence of best subtrees, as a function of $\\alpha$.\n",
@@ -584,118 +555,10 @@
{
"cell_type": "code",
"execution_count": 2,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- " mean radius mean texture mean perimeter mean area mean smoothness \\\n",
- "0 17.99 10.38 122.80 1001.0 0.11840 \n",
- "1 20.57 17.77 132.90 1326.0 0.08474 \n",
- "2 19.69 21.25 130.00 1203.0 0.10960 \n",
- "3 11.42 20.38 77.58 386.1 0.14250 \n",
- "4 20.29 14.34 135.10 1297.0 0.10030 \n",
- ".. ... ... ... ... ... \n",
- "564 21.56 22.39 142.00 1479.0 0.11100 \n",
- "565 20.13 28.25 131.20 1261.0 0.09780 \n",
- "566 16.60 28.08 108.30 858.1 0.08455 \n",
- "567 20.60 29.33 140.10 1265.0 0.11780 \n",
- "568 7.76 24.54 47.92 181.0 0.05263 \n",
- "\n",
- " mean compactness mean concavity mean concave points mean symmetry \\\n",
- "0 0.27760 0.30010 0.14710 0.2419 \n",
- "1 0.07864 0.08690 0.07017 0.1812 \n",
- "2 0.15990 0.19740 0.12790 0.2069 \n",
- "3 0.28390 0.24140 0.10520 0.2597 \n",
- "4 0.13280 0.19800 0.10430 0.1809 \n",
- ".. ... ... ... ... \n",
- "564 0.11590 0.24390 0.13890 0.1726 \n",
- "565 0.10340 0.14400 0.09791 0.1752 \n",
- "566 0.10230 0.09251 0.05302 0.1590 \n",
- "567 0.27700 0.35140 0.15200 0.2397 \n",
- "568 0.04362 0.00000 0.00000 0.1587 \n",
- "\n",
- " mean fractal dimension ... worst radius worst texture \\\n",
- "0 0.07871 ... 25.380 17.33 \n",
- "1 0.05667 ... 24.990 23.41 \n",
- "2 0.05999 ... 23.570 25.53 \n",
- "3 0.09744 ... 14.910 26.50 \n",
- "4 0.05883 ... 22.540 16.67 \n",
- ".. ... ... ... ... \n",
- "564 0.05623 ... 25.450 26.40 \n",
- "565 0.05533 ... 23.690 38.25 \n",
- "566 0.05648 ... 18.980 34.12 \n",
- "567 0.07016 ... 25.740 39.42 \n",
- "568 0.05884 ... 9.456 30.37 \n",
- "\n",
- " worst perimeter worst area worst smoothness worst compactness \\\n",
- "0 184.60 2019.0 0.16220 0.66560 \n",
- "1 158.80 1956.0 0.12380 0.18660 \n",
- "2 152.50 1709.0 0.14440 0.42450 \n",
- "3 98.87 567.7 0.20980 0.86630 \n",
- "4 152.20 1575.0 0.13740 0.20500 \n",
- ".. ... ... ... ... \n",
- "564 166.10 2027.0 0.14100 0.21130 \n",
- "565 155.00 1731.0 0.11660 0.19220 \n",
- "566 126.70 1124.0 0.11390 0.30940 \n",
- "567 184.60 1821.0 0.16500 0.86810 \n",
- "568 59.16 268.6 0.08996 0.06444 \n",
- "\n",
- " worst concavity worst concave points worst symmetry \\\n",
- "0 0.7119 0.2654 0.4601 \n",
- "1 0.2416 0.1860 0.2750 \n",
- "2 0.4504 0.2430 0.3613 \n",
- "3 0.6869 0.2575 0.6638 \n",
- "4 0.4000 0.1625 0.2364 \n",
- ".. ... ... ... \n",
- "564 0.4107 0.2216 0.2060 \n",
- "565 0.3215 0.1628 0.2572 \n",
- "566 0.3403 0.1418 0.2218 \n",
- "567 0.9387 0.2650 0.4087 \n",
- "568 0.0000 0.0000 0.2871 \n",
- "\n",
- " worst fractal dimension \n",
- "0 0.11890 \n",
- "1 0.08902 \n",
- "2 0.08758 \n",
- "3 0.17300 \n",
- "4 0.07678 \n",
- ".. ... \n",
- "564 0.07115 \n",
- "565 0.06637 \n",
- "566 0.07820 \n",
- "567 0.12400 \n",
- "568 0.07039 \n",
- "\n",
- "[569 rows x 30 columns]\n",
- " malignant benign\n",
- "0 1 0\n",
- "1 1 0\n",
- "2 1 0\n",
- "3 1 0\n",
- "4 1 0\n",
- ".. ... ...\n",
- "564 1 0\n",
- "565 1 0\n",
- "566 1 0\n",
- "567 1 0\n",
- "568 0 1\n",
- "\n",
- "[569 rows x 2 columns]\n"
- ]
- },
- {
- "data": {
- "text/plain": [
- "32512"
- ]
- },
- "execution_count": 2,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
"source": [
"import os\n",
"from sklearn.datasets import load_breast_cancer\n",
@@ -742,19 +605,10 @@
{
"cell_type": "code",
"execution_count": 3,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "32512"
- ]
- },
- "execution_count": 3,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
"source": [
"# Common imports\n",
"import numpy as np\n",
@@ -782,6 +636,60 @@
"os.system(cmd)"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Other ways of visualizing the trees\n",
+ "\n",
+ "**Scikit-Learn** has also another way to visualize the trees which is very useful, here with the Iris data."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "from sklearn.datasets import load_iris\n",
+ "from sklearn import tree\n",
+ "X, y = load_iris(return_X_y=True)\n",
+ "tree_clf = tree.DecisionTreeClassifier()\n",
+ "tree_clf = tree_clf.fit(X, y)\n",
+ "# and then plot the tree\n",
+ "tree.plot_tree(tree_clf)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Printing out as text\n",
+ "\n",
+ "Alternatively, the tree can also be exported in textual format with the function exporttext.\n",
+ "This method doesn’t require the installation of external libraries and is more compact:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "from sklearn.datasets import load_iris\n",
+ "from sklearn.tree import DecisionTreeClassifier\n",
+ "from sklearn.tree import export_text\n",
+ "iris = load_iris()\n",
+ "decision_tree = DecisionTreeClassifier(random_state=0, max_depth=2)\n",
+ "decision_tree = decision_tree.fit(iris.data, iris.target)\n",
+ "r = export_text(decision_tree, feature_names=iris['feature_names'])\n",
+ "print(r)"
+ ]
+ },
{
"cell_type": "markdown",
"metadata": {},
@@ -928,78 +836,11 @@
},
{
"cell_type": "code",
- "execution_count": 4,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- " (0, 0)\t1.0\n",
- " (0, 7)\t1.0\n",
- " (0, 9)\t1.0\n",
- " (0, 13)\t1.0\n",
- " (1, 3)\t1.0\n",
- " (1, 5)\t1.0\n",
- " (1, 8)\t1.0\n",
- " (1, 12)\t1.0\n",
- " (2, 3)\t1.0\n",
- " (2, 5)\t1.0\n",
- " (2, 8)\t1.0\n",
- " (2, 11)\t1.0\n",
- " (3, 1)\t1.0\n",
- " (3, 5)\t1.0\n",
- " (3, 8)\t1.0\n",
- " (3, 12)\t1.0\n",
- " (4, 2)\t1.0\n",
- " (4, 6)\t1.0\n",
- " (4, 8)\t1.0\n",
- " (4, 12)\t1.0\n",
- " (5, 2)\t1.0\n",
- " (5, 4)\t1.0\n",
- " (5, 10)\t1.0\n",
- " (5, 12)\t1.0\n",
- " (6, 2)\t1.0\n",
- " :\t:\n",
- " (8, 12)\t1.0\n",
- " (9, 3)\t1.0\n",
- " (9, 4)\t1.0\n",
- " (9, 10)\t1.0\n",
- " (9, 12)\t1.0\n",
- " (10, 2)\t1.0\n",
- " (10, 6)\t1.0\n",
- " (10, 10)\t1.0\n",
- " (10, 12)\t1.0\n",
- " (11, 3)\t1.0\n",
- " (11, 6)\t1.0\n",
- " (11, 10)\t1.0\n",
- " (11, 11)\t1.0\n",
- " (12, 1)\t1.0\n",
- " (12, 6)\t1.0\n",
- " (12, 8)\t1.0\n",
- " (12, 11)\t1.0\n",
- " (13, 1)\t1.0\n",
- " (13, 5)\t1.0\n",
- " (13, 10)\t1.0\n",
- " (13, 12)\t1.0\n",
- " (14, 2)\t1.0\n",
- " (14, 6)\t1.0\n",
- " (14, 8)\t1.0\n",
- " (14, 11)\t1.0\n",
- "Train set accuracy with Decision Tree: 0.73\n"
- ]
- },
- {
- "data": {
- "text/plain": [
- "32512"
- ]
- },
- "execution_count": 4,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "execution_count": 6,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
"source": [
"# Common imports\n",
"import numpy as np\n",
@@ -1086,73 +927,11 @@
},
{
"cell_type": "code",
- "execution_count": 5,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "X1 < 0.000 Gini=0.408\n",
- "X1 < 0.000 Gini=0.408\n",
- "X1 < 1.000 Gini=0.394\n",
- "X1 < 2.000 Gini=0.394\n",
- "X1 < 2.000 Gini=0.394\n",
- "X1 < 2.000 Gini=0.394\n",
- "X1 < 1.000 Gini=0.394\n",
- "X1 < 0.000 Gini=0.408\n",
- "X1 < 0.000 Gini=0.408\n",
- "X1 < 2.000 Gini=0.394\n",
- "X1 < 0.000 Gini=0.408\n",
- "X1 < 1.000 Gini=0.394\n",
- "X1 < 1.000 Gini=0.394\n",
- "X1 < 2.000 Gini=0.394\n",
- "X2 < 0.000 Gini=0.408\n",
- "X2 < 0.000 Gini=0.408\n",
- "X2 < 0.000 Gini=0.408\n",
- "X2 < 1.000 Gini=0.407\n",
- "X2 < 2.000 Gini=0.407\n",
- "X2 < 2.000 Gini=0.407\n",
- "X2 < 2.000 Gini=0.407\n",
- "X2 < 1.000 Gini=0.407\n",
- "X2 < 2.000 Gini=0.407\n",
- "X2 < 1.000 Gini=0.407\n",
- "X2 < 1.000 Gini=0.407\n",
- "X2 < 1.000 Gini=0.407\n",
- "X2 < 0.000 Gini=0.408\n",
- "X2 < 1.000 Gini=0.407\n",
- "X3 < 0.000 Gini=0.408\n",
- "X3 < 0.000 Gini=0.408\n",
- "X3 < 0.000 Gini=0.408\n",
- "X3 < 0.000 Gini=0.408\n",
- "X3 < 1.000 Gini=0.367\n",
- "X3 < 1.000 Gini=0.367\n",
- "X3 < 1.000 Gini=0.367\n",
- "X3 < 0.000 Gini=0.408\n",
- "X3 < 1.000 Gini=0.367\n",
- "X3 < 1.000 Gini=0.367\n",
- "X3 < 1.000 Gini=0.367\n",
- "X3 < 0.000 Gini=0.408\n",
- "X3 < 1.000 Gini=0.367\n",
- "X3 < 0.000 Gini=0.408\n",
- "X4 < 0.000 Gini=0.408\n",
- "X4 < 1.000 Gini=0.405\n",
- "X4 < 0.000 Gini=0.408\n",
- "X4 < 0.000 Gini=0.408\n",
- "X4 < 0.000 Gini=0.408\n",
- "X4 < 1.000 Gini=0.405\n",
- "X4 < 1.000 Gini=0.405\n",
- "X4 < 0.000 Gini=0.408\n",
- "X4 < 0.000 Gini=0.408\n",
- "X4 < 0.000 Gini=0.408\n",
- "X4 < 1.000 Gini=0.405\n",
- "X4 < 1.000 Gini=0.405\n",
- "X4 < 0.000 Gini=0.408\n",
- "X4 < 1.000 Gini=0.405\n",
- "Split: [X3 < 1.000]\n"
- ]
- }
- ],
+ "execution_count": 7,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
"source": [
"# Split a dataset based on an attribute and an attribute value\n",
"def test_split(index, value, dataset):\n",
@@ -1257,38 +1036,11 @@
},
{
"cell_type": "code",
- "execution_count": 6,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "(426, 30)\n",
- "(143, 30)\n",
- "Test set accuracy with Logistic Regression: 0.95\n",
- "Test set accuracy with SVM: 0.63\n",
- "Test set accuracy with Decision Trees: 0.90\n",
- "Test set accuracy Logistic Regression with scaled data: 0.96\n",
- "Test set accuracy SVM with scaled data: 0.96\n",
- "Test set accuracy with Decision Trees and scaled data: 0.90\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "/Users/MortenImac/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/_logistic.py:762: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
- "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
- "\n",
- "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
- " https://scikit-learn.org/stable/modules/preprocessing.html\n",
- "Please also refer to the documentation for alternative solver options:\n",
- " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
- " extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n"
- ]
- }
- ],
+ "execution_count": 8,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
"source": [
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
@@ -1342,22 +1094,11 @@
},
{
"cell_type": "code",
- "execution_count": 7,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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DVERERERiQQ1TEREREYkFNUxFREREJBbUMBURERGRWFDDVERERERiQQ1TEREREYmFyFd+kvx0di6kvf1meno20tAwiba2q2lpOT/qskREYk3ZKZIMapgmSGfnQlavvoL+/j0A9PS8yOrV3kqDClgRkWDKTpHk0KX8BGlvv/lAsA7o799De/vNEVUkIhJ/yk6R5FDDNEF6ejbmtV1ERJSdIkmihmmCNDRMymu7iIgoO0WSRA3TBGlru5qamsZDttXUNNLWdnVEFYWrs3MhixefwqJFrSxefAqdnQujLklEEkDZqeyU5NDgpwQZ6KRfjSNLNXhBRAql7FR2SnKoYZowLS3nV2WYZBu8UI2fh4jkR9l5kLJT4kwNUxkkjvP9afCCiMSdslOkeOpjKocYuOzT0/Mi4A5c9om6T5IGL4hInCk7RUpDDVM5RFzn+4tq8MLAoIHHH5/GiScu4Oijnyvr+4lIMik7D6UBV1IoXcqXQ8T1sk8UgxfSBw0MH97NmWf+gf3PncymfceU7X1FJHmUnQdpwJUUQw1TOURDwyT/UtTg7RBtH6qwBy8EnQGpr+9l7oxnuOdZNUxF5KBs2Rl139M4ZKcGXEmudClfDpHtsk9c+1CVS6YzHc3DXwm5EhGJu0zZOWbMWVWVmxDfs8eSDGqYyiFaWs5n+vSv0dAwGTAaGiYzffrXaGk5P7Z9qMol0+CAXXubQq5EROIuU3Zu3/5gVeUmaMCVFEeX8mWQTJd9sh0FNzYGPpRobW1XH9JPCmD//joeXfWaCKsSkbgKys6VKy8L3LeSzx4GZWc1rbQlxdEZU8lZtR0Fp58B2bt3BA89dA6rXjoy6tJEJCGqLTch+5U3kaHojKnkLNtRcGdnhIWV0cAZkL179/Ktb93ImjXj4Mh1UZclIglRrWcPq3WlLSmezphKznQULCKSH+WmSH50xlTyoqNgEZH8KDdFcqczpiIiIiISC2qYioiIiEgsqGEqIiIiIrGgPqYVIuol70REkkjZKRIvaphWgIGlQgemIxlY8g5IVMDqD4SIhEnZKRI/kV/KN7PLzGyJmfWY2Z1D7PsZM9tkZjvM7A4zawipzFirhKVCB/5AVNN60iLFUHYWT9kpEj+RN0yBDuAm4I5sO5nZOcBVwFlAGzAN+HK5i0uCbEuFJkW+fyA6OxeyePEpLFrUyuLFpyiEpRopO4uk7FR2SvxE3jB1zi10zv0a2DbErh8CbnfOLXfObQduBC4ud31JUAlL3uXzB0JnCESUnaWg7FR2SvxE3jDNw/HA0pT7S4EWMxsXtLOZXeJf5lqyZctQuZ1sbW1XU1PTeMi2pC15l88fiFzPEOjMgAig7MxI2Tk4O5WbErUkNUxHAjtS7g/8vzloZ+fcbc652c652RMmBOZvxaiEJe/y+QORyxkCnRkQOUDZmYGy89Dtyk2JgySNyt8NjEq5P/D/XRHUUlKlGFGZ9CXvBmrP5XNoaJjkB+fg7QOynRlI8uckUgBlZxbKzoPZqdyUOEhSw3Q5MAv4pX9/FtDpnEv0taZKma6kFHL9A9HWdvUhnxkMPkNQCYMaREpE2VnhSpWdyk2Jg8gbpmZW59dRC9Sa2XCg1znXm7brz4A7zexu4CXgGuDOMGstxFBH9HE7Qi30DMRZZ72erq63Ddre2LiDefN+UNIaczlDkMtZVZEkq+TszCWHKiU7J04cxZYtTYO2t7T00dGxpaQ1DpWdyk2Jg8gbpngheV3K/YuAL5vZHcBzwHHOufXOud+b2S3An4FG4J6058VOLkf0cTpCLfQMRGfnQrq6Lg18bM+e0aUvlKHPEORyVlUk4SoyO3PNoUrJzi1bgrOzs7O29IWSPTuVmxIHkQ9+cs5d75yztNv1fqCOdM6tT9n3G865FufcKOfch51zPVHWPpRcRkDGabqSQiebjuNk1JUwqEEkm0rNzlxzSNlZespNiYM4nDGtWLkc0cfpCLXQMxBDPd7S8gVWrVrBjBlfLbi2QiR9UININco1h6ohOxctmszEiReFmp3KTYla5GdMK1kuR/RxOkIt9AzEUI+bOTZt+imrVl1VcG2g+fVEqkGuOVQN2Ql9yk6pOmqYllGu88u1tJzPnDlPcMYZHcyZ80RgsIYRLIVONp3rGYpNm35ecG2aX0+kOuSTQ8rOoSk7JWnUMC2jUh3RhxUsxdQ7Zkxnhu2bUu71FVxboX24RCRZSnkmNAnZOXasslMklfqYllkp+uuEOS1KvvUOBP/ChZ88sM05MAvau/BRpnEagSsi5VWqfo5JyM577vnk0DsrO6WK6IxpAsQ5WIKC38xrnKabOPGigt8nTiNwRSQZkpadmSg7pZqoYZoAcQ6WbAHf3284B84ZEyd+qKiRpYX24RKR6pXU7Dx4hrRW2SlVR5fyEyBoWhSAvr5uOjsXRjq1R6aVQnbtbeT2+y5iSl0N8+a1ceaZFxf1PvmsBx21UqzfLSLFS2J2NjRMZs6cJ0r2PspOSRo1TBNg4Bfz+eevoa9v+4Htvb3bI18bOij49/fV8te1x5f8vZIwv97IkctZvfqbWr9bJAaSlp3lOpOZhOwsdPUsqTy6lJ8QLS3nU1c3YtD2qEdXDoxGNZuAc7BzVzP/8/cTWdU5NbKaClWKaWXGjfuLRsCKxEjcszMO87AWo1TTcWn2ABmgM6YJEmZH/n379nHffb/gxRfbc9q/r+/ddHT00767DjtiM1YD1rCX7l2jef75Vdx66w0lr7EUhg1r4K1vvYCmpmVZj9a3bv01J564gNNO62bX3iae6jgp8PXq6nYGbo/DYAuRahVmdu7YsZ177vk5O3ZsH3pnAN5x4H9//euzwLMlr6kcmptH88Y3HkZHx5eznuXM9fJ8nAeqSbjUME2QzH2SStuRf9u2Lfz0p7ex9Jkaunsbcn5e/+gdWEsXw+obGDGyie1929le28uTKw7HXOD8UZGrxWhv/ynveMe9QOaj9fb2LzB8uPf4qMZXmNf2GLs674WGMw95Tm/vKOrrBzdO4zDYQqRahZWda9as5K677mLV2mb2udyzM4nqrYcpU26ksTHzdFz5XJ4P63sk8aeGaYLk0yep0E7kmzZt5Pbbv8uyv49h/5SNWF0v5NimNGDsuLFc/sFP0jyimR/91+2sWrkK17aBgNmjYqHXwZrN43FuW+Dcqz09GwMvMdXV9tHVfgvNxxzaMN22bT6trX+KxfrdIuIJIzuXLXuSBQv+ixXrR+OmbsBq45p6pdHTbwwf3h38mH+WM595ZMPscyvxpoZpguQ6urKYTuSrV69g505jf30fNcN6mdw6ibbJbTnVN6p5FG943RsYVj8MgEv/z8d5YtkTrHtxXT5fZmj29+7nqWefZt/oXXR3NzNy5K5B+3hH8cGXknp7OgZt2737eKZPP0cjS0ViJIzsfO65pezc2UB/czd1w4wZ06YzYeyEMnw10et6uYsVz69k1ysjGDVicON04CxnPpfnkzR7gJSXGqYJk8voylKsdmIAZpw08yTOmntWQbWaGafOOpVTZ51a0PPLraenh2UrnmGf9bNkyTxe//o/UVvbe+DxgaN1LygHX2Kqa2gNfN0kjIAVqTZhZOfAVRcz4+x5ZzP9qOkF1xtn615cx8q1q3n02ZM5++S/Ulc3ODch/8vzyk4BjcqvSOpEnr+1a1/NCy+cEThCNmiC6t6+Wsa2fT6aYkWkLJSd+Vm54Wgef3w+vb2jCZpZQJP7SyF0xrQCqRN5Ybq6pjNnzt2Dtre0nM/+/ft47rlraGg4OCr/o294N10vd0VQqYiUg7Izf+vXz6C19XQ++MFPDHpMl+elEGqYViB1Is/N69ra2Lr554dsu/JKaGnpo6NjyyHbx49/F3/72zMsWzaOvUeuY9S44WGWKiIhUHbm5ryTT6Vr64ID9//93+HDHw7OTl2el3ypYVqBdJSam62bg3/8OztrA7eLSGVTduama+uwwO3KTikFNUzLKMp1f3WUKiJJpewUqV5qmJaJ1v0tzMmtrWwJOOqe0NLHkx2Dp2cSkcqi7CyMslMqhUbll4nW/S1MULBm2y4ilUXZWRhlp1QKNUzLRNOOiIjkT9kpUt3UMC2TTNOLaNqR+Bh/eG/g9paWvpArEZEBys74Gzt+X+B2ZaeUgvqYlommHYm/x9rbue6b1/NKVz/NnUcwc2Y3V1xxfUleO8rBGyJJpuyMv/uefJxv/eQ77N8ygom9jZx22vjAeUwLoewUNUzLRNOOVC8N3hApnLKzeik7BdQwLStNO5K/CS19GUeWJkWx622LVDtlZ/6UnVIp1DCVWKmEaU00eMOj6WtEwlMJv1PKTuUmaPCTSMlp8IZH09eISD6UncpNUMNUpOTa2q6mpqbxkG0avCEikp2yU0CX8kVKToM3RETyp+wUUMNUpCxKMXijVNOmpL9Off189KsvInEU5+yEtxZVl+RGl/JFYmhg2pSenhcBd2DalM7OhUW/TlPTAiZMWFOWukVEolTO7IQ7aW1VdpZbLBqmZjbWzO41s24zW2dm78+w38Vm1mdmu1Nu80MuV6TsSrVeeNDrmO1n2rSniq5xKJmmqUnS9DVxp+wUOVR5s3Mfxx67pOgas1Fuxud63veAfUALcALwgJktdc4tD9j3MefcvFCr82lFCglLqaZNybR/Q0N33jXlq1qmNolY7LNTuSlhKnd2NjaWNzuVmzFomJrZCOACYKZzbjfwiJndD3wAuCrS4lJoRQoJU0PDJP/S0eDtpXidnp4RBddWiELn5tOcfpklITuVmxK2cmfnnj3xz86k52YcLuXPAPqcc6tSti0Fjs+w/4lmttXMVpnZtWYW2Lg2s0vMbImZLdmyZVvRRZbq8oBILko1bUrQ6zhXz9q1JxVdYz4KnZsvijn9Tm5tZWrtlEG3k1tby/aeBYp9dio3JWzlzc5hrFgxu+ga81FIBkY1F2qpsjPyM6bASGBH2rYdQHPAvouAmcA6vPBdAPQCg1LOOXcbcBvA7NmzXLFFakUKCVOppk0Jep2urvls2RKHX/14StAE17HPTuWmhK2c2bl371vp6Ogtec2VolTZGYe/TruBUWnbRgG70nd0zq1NufuMmd0A/F8CwrXUSnV5QOJp2rS/M2vWwyxadFts+sGVar3w9Ne5//4FwNNFv27cJP3yVQFin53Kzcp2zJQ1nDHzKZqadrN48R2xyE0oX3Y+9NAf8I7xKkvcsjMODdNVQJ2ZTXfOrfa3zQKCOu+nc4CVrbIUbW1XH9JXCrQiRaWY0drO3JlPUF/vHQln6gf3la98g927Rx+4/7PPA0xh+Mgf8JaPXR5myRIgQWc6SyX22ancrFzTJ6zjDTOWUF/njRbP1n+4tXUCnQG/hy0tfXR0bCl/sZJV3LIz8j6mzrluYCFwg5mNMLO5wHnAXen7mtm5Ztbi//9Y4FrgvjDqbGk5n+nTv0ZDw2TAaGiYzPTpX4vF0aEUZ+4xSw80SgcE9YNLbZSm2rv7sLLVJpJJErJTuVm5Tpv2zIFG6YBM/YeDGqXZtkt1i8MZU4BLgTuAzcA24BPOueVmNhV4DjjOObceOAu408xGAp3Az4GvhFVkqS4PSLw0N74SuL2n50UWLz6Fnp6NDBvWCgy+JCm5mdDSl/FSUTmeV0Vin53KzcrU3JApNzcGrJi0IdziKkghGZj03IxFw9Q51wW8K2D7erwO/gP3rwCuCLE0qQK79jQxqikoZO1A/7h9+zRYoxiF9lOKon9TkkJd2SlR2dXTxKjhg3OztvawQVOESeEKycCo+tSXKjtj0TAVicrR416grnY/zoEd0uPO8LrhSZSi6JRfoQOlREqqfesRvGbSmkNys6amETPo69uT+YkSiiRnZ059TM2s0cxeNLP1ZtaQ9tiP/aXu/rEkFYWos3MhixefwqJFrSxefErgWrq57CPJ1L31fl5/9GKaGvYfEq51dWNQozQe8umUH8el/Ko1O5Wbla1v1x959RHtgw7mDz/8ffT2vhxVWZIiydmZ0xlT59weM7sO+DFen6ZbAczsZuCjwCedc78oW5VlkMuKJFq1JHdxm24iFzs3fJ362sG/eLW1I6itHTHoEtSYMZvYvn3ioP2Hj1QQx0Ecf86qMTuVm/lJYna67Z96BUUAAB1qSURBVLcFZKdj+/YHA6cIy5SdLTHsHlON4vZzls+o/DvxpiG52sxGmtmn8Za9u8459/1yFFdOuaxIolVLche36SYGZFuJom/fS4HP6enZGLjqx69+NZkvfvHtvOvKf+CDt3yI9X0beHrbUs676hNhfCmSXHdSRdmp3MxPErOT3s2Bz8mUnb/+9TQ6Or5PX9+mQ26aKkqC5Nwwdc714YXpBODXwDeA7zjnbihTbWWVy4okpV61RJe3wpct9GuHHRH4WEPDpEHT3AwbNok1a+axZs1xZaxWKlG1ZadyszJkbTDXHR74WFB2aoowyVde85g6534DPIU39cgC4FOpj5tZg5n9yMzWmtluM1vtnx2InUwrj6Ruz2WfXA1c3vIucbgDl7cUstEZNeVz7O87NHxTJ/9uaTmfOXOe4IwzOjjhhEfYtu3oKMqUClBN2ancrHw25pKcs3POnCfUKJW85NUwNbP3ASf4d3c559JHiNQBm4A34y2N9168y1f/UGyhpRZ0uSF9RZJc9smVLm/Fz4jx7+ThNXPY+UoTzkFPz0gd2cdM3DrlF6qaslO5Wflqm9/Mn1fOZmf3CJyD3t7Rys6YSXJ25jxdlJm9GW9FkXuB/cBHzOxW59zfB/bxVyK5NuVpT5vZA8BcvLMEsTHwC5Q6CXD6Or+57JOrUl/ekuwyDShIt2bbUTyz+kiaO49g5sxuzj47+/f26KOf49S5f6Z5+CusW/wgww7/ZKlKlgBx65RfiGrLTuVmsuWanau3HMlzzx3HxN5GTjttPG98Y/bvb/qk+4X+TEhukpydOTVMzWwO3tJ3jwIXApOBC4CbCZjcOeV5dcA84JaiKy2DXFYkKdWqJUEjFQe2V4K4TUpejoED48atYfbsRw8sX9rbs5HeF2/gVeNPYPnWV5X8/cKWxNHBcVet2anczF01ZGelz9Sg7CytIRumZvZq4AFgFfAu51wPsMbMbgc+bmZznXOPZnj6t4EdwM9KVXBStbVdfcgvJhR+eSuOovzly/UIP1UhoT916pIDjdID3F7mHLmM5SuS3zCN6+jgpFJ2Fq/ScxOqIzuzdcmohIapsrO0svYx9ddb/iNeQJ7rnNuZ8vANwB4yHNGb2dfxjvjPdc7tK025yZVppCKgEadFyveXf33fhkF/DKZN+zuzZt2d9fvQ0NAd+HojM6wZHTaNXo4PZWdpKDfLqxTZOXXqKiZP/lbW70Xcu2QoO+Ml6xlTf73lKRkeewloCnrMzL6JN/r0jc65rcUWWSnSL29V+uWNpJjR2s7cmU8cOBua6fvQ0zOC4cMHN0539wT+GpRdap+t2trD6O/fjXP7Af0sRU3ZWTrKzfg6ZsoaTj35r9TVZc/OuHXJUHbGW16j8nNhZt8G3oQXrJo9NwuNOI2HuccsHXSJPuj7sH79bPbvTzuWs+EsXvfacpc4SPo0On192w8E6wD9LCWLsjM3ys34mDvzyQON0gFB34tSztRQLGVn/JW0YWpmRwKXA68CXvDn49ttZr8r5fsk3cBlg6AjSIjP5Y1KFNQ/qrkx+FJ8+vdh27ajeeihc9i5x5teqq5hEk2Tv8TzW9vKUWpWQX+cg+hnKRmUnbnp7Fyo3IxIYHY2BXdvSv9exGnSfWVn/OU8XVQunHPrACvla1aa9MtQQSppxGnU1vdtGHKfXXuaGNU0uHEa9H1Ys+Y4lveOYNS44dz0uZvoerkLWFaKUvOSa2jm87MUt9HB1UTZObSB7MxEuVlaOWXnKyMYNWJw4zToe1GqmRqKpeyMv5I2TGVoQx2tRXF5I7W/DYyltfV4nt98wpDPi4tiQ+HRlbN4U0ofU4j/yN9MfbZS5fs1aFoTibNs2RnlZeGB7Dz88NFMmXISG16YFnodhSo6O589mbNT+piCslOKp4ZpyLIdrTU0TA590uHBZ3C38ZrXPMqmHY2s7h8ZWh3FKDYUVnW00bjjMF73uodpaOhOxOTPQdPoQD11dSPp7X05EV+DSD6yZWcUl4XTs7Ou7mVOPnkR2/r6WbtvQqi1FKrY7Fy54WhG9w3j1FOfoK5uZyJyR9kZf2qYhizz6MTJzJnzROj1BJ2FqKvrY+7sv7L68TeHXs9QyjWR8dq1r6apaSpXXHF9EdWFp5Sr64gkQbbsjEtfxbq6XubNWsLaJ84NvZ6hlCs716+fQWvr6Xzwg58oprzQKDvjTw3TkMVtwuhMZyGaR+wq+DXLuQqGJjI+KC59tkTCkJjsbNpd0OuVe/UgZedBys54K/l0UZJdnEYnQuYO3ru6mwt+TQWgiJRaYrLzlcK6QCk3RTw6YxqBOB2tBZ2F6O2t5dElp0dYlYjIYPHPzjoeWTo7wqpEkk9nTEsgycuZpZ+FgHE888xcVr1wTNSliUiFq6Ts7O09jCefPIOV618VdWkiiaYzpkWqhOXxUs9CPPzwg3R0PBhxRRJn5e4LJ9Wh0rLzF7+4gw0b1gOau1KCKTtzozOmRQp7ebwkn2EohUzz62ki4/CoL5yUgrIzXMrO6Ck7c6MzpkXKNDKzHMuZhXWG4fvfv5VXXhkNwL03Htye61FdOVfBqMajytRJvAuZ2iT9+fX189GvvkSt0rLz0kv/hR07Dg58uufL3r9xyE1QduabnUHPhRHlLVgA/XUqWua59Uq/PF62MwylbJgONErT5XpUV40BWC7F/kENen5T0wImTDgNVowvX+EiQ6i07ExtlKZSbkajmOzM/NwPlLVm8ahhWqQw59YL8wyDHGrr1l9z4okLOO20bnbtbeKpjpNCed9i/6AGPd9sP9OmPQUPzylprUEy9alKpf5V1UnZWR2KveJTqGKyM9Nz4R7gvBJXGqyas1N9TIsU5tx6mc4klOMMgxzkBesXGD68GzMY1fgK89oeY1fnvWV/72L/oGbar6Ghu+Ca8pHL2SL1r6pOys7KN3Dm0Tsz7g6ceQyjf28x2Zl5n21FVJSfas5OnTEtgbDm1ovbyifVInDpwdo+utpvofmYM8v63sVe7sz0/J6ewvtKlbsvnFQPZWdlC6v7WZBisjPTc2FcUTUpO3OjhmmCaI3faGQ6eu7tKf8llGL/oAY937l61q4tvCtCJV46ksqm7IxGlF0oisnOTM/t67sA6C24JmVnbtQwTZgwzjA0Ne0IHABV+Ud1ji1bOgdtraubSG/vS4O3N7SWvaJi/6AGPb+raz5btuhXX6pLubNz9OjdgQOgKj83MwtzgFu6YrIz03NXrBgBLCpn2YIaphLg0ks/w/Obm7EjN/LOs9/OWXPPirqksqipqaGmpgbqe9hVs4+XXurj1lu/OWi/I454Fccfv5m6uoN/YHr7azm87fNQV0+NGQzv4eWtjhUrOvnSl75UhmrPTfn/0/6tsOf39/exZWsjbtw2MEfDsIZSFBg5TV4tUfr+97/NX/+6ng29fdRP3MGlH/g404+aHnVZkYq6C0UxByNBz12x4g+lKCt24padapimiWoEoYSvvr6eC95yPnff+wt6J3Xwwo5m3Cv1g/b7+/Zj2Nhdy7xZT9Dc1M3uniYaJ15Oc8u7AXjbG97Gvb+/j94pG1m9YzTs6g/ta/jDXV+nZ8/gs9sNjTs45wOfy/Asw5q3YY37GD9uHOe+4dwM+xUvU5+q9H1KQZNXR0vZKeni3IWitXUCnQHZ0NLSR0fHlggqOlQ1Z6capinKOQmzQjueTn7NybS2tPL9n/+QnbU7M+73/N5xrFn8FsaOG8dlH7iUsYeNPfDYGXPOYGrrVH5494/ZU5f5NcohqFE6sL225eWMzzMzZr36BC5694XU1w9ujJeKzlRWB2WnZBLWALd8BTVKs20PWzVnZywapmY2FrgdeDOwFbjaOfcfGfb9DHAl0Ig3qdgnnHM9paijXCMIK2FN6Ep2xOFHcO1lX2Rj50accxn3q62pZdLESdTVDf61aZvSxnWf/iIvbR7cF7WcUlfmSvepj1ye8bHhDcM54vAjMLMyVCVhUXaKSKWJRcMU+B6wD2gBTgAeMLOlzrnlqTuZ2TnAVcAbgQ7gXuDL/railWsEYZRTZkhuhg0bxlFTjirqNRqHNzJt6rQSVVS8ONUiZaPsFJGKEvkE+2Y2ArgAuNY5t9s59whwP8Frf30IuN05t9w5tx24Ebi4VLWUaxJmrToiIqWm7BSRShR5wxSYAfQ551albFsKHB+w7/H+Y6n7tZjZoFlvzewSM1tiZku2bMlttYa2tqupqWk8ZFspRhBq1RGR8ss0EKCCp+tRdopI0eKWnXG4lD8S2JG2bQfQnMO+A/9vJm2tMOfcbcBtALNnz8rccTBFuUYQRj1lhlQurSRyUBUOFlB2ihSopaUv46j8ahO37IxDw3Q3MCpt2yhgVw77Dvw/aN+ClGMEYZynzJBki1ugSKiUnSIFisOUUBIsDg3TVUCdmU13zq32t80Clgfsu9x/7Jcp+3U653K73hShuE6ZISKJpewUkYoTeR9T51w3sBC4wcxGmNlc4DzgroDdfwZ81MyOM7MxwDXAnaEVG5HOzoUsXnwKixa1snjxKXR2Loy6JBGJmLIzO+WmSDJF3jD1XYo3t95m4D/x5tdbbmZTzWy3mU0FcM79HrgF+DOwzr9dF1HNoRiYx89bb9gdmMdPISsiKDsDKTdFkisOl/JxznUB7wrYvh6v037qtm8A38jv9aGvL5kdmtvbv5JhHr+vMH78eSV/P+fCW04zV4Wu4xu39X9FSk3ZGSzs3ASyLs4RlUIysJDnOOcS+XOSr/7++H2PK1EsGqbl1tn5El/96jVRl1GQefM2ErQ4z969G8vyNTnneHnHCFzzTgyorQ1eni3MRl+h6/jGbf3fSqDGfnXZtKk8OVNuYecmQG9vP9t3HoZN2ApATc3gC5Jh//4UkoG5PKempsb7fIfvZUfnKNrb1yXy5yRfzjm2vzwSmr0xg7U1uf8tUXbmrioapt17jaeeHzn0jjE068RmRo0cPHB2V3dz2b4mN64La9rLmDFjOen4kwL3UaOvOun7Xl1e6alJZHZGkZsYuImbsGG9HHnkUUydNHXQLpXy+9Pa0spR045i1crV7GntYNm6w8FVx/LGbtw2rKmHcWPHccJxJ+T8vEr53oehKhqmDNsPU5N5RPJo+zG86dV/o7724GWS/X21PNp+TNm+phqM6TNmcMl7P8awYcPK8h4ikgAJzc4ochOg1mp449yzePtZbws8Y1opamtrufQfP8GfHv4Tv/vLH+mf8lLUJYWmBuOYY4/lYxd8RH8fy6QqGqZjDxvD+ecO6oaVGDv7/srovl9Ryzb6GMfOYe9l5kmnM7NM7zduzDhmzpiJBV0LE5GqcdiowxKbnWHnJkDbpDbaprSV8R3io6amhnPOPIfjph/HmvVroi4nNBPGTOC4Gcfp72MZVUXDdETTCOafNj/qMoowH/hC1EVIHtSfSCpB88jmBGfnfJSb5TeldQpTWqeU7PWUnVK51xqkYhS6jm+U6/9WYn+ik1tboy5BRPJQSAZGvW66slOq4oyplF6Ya7QXepSso+vSyvaHIaw/WiJJFmZuQmEZqNwsPWVnftQwlYIovCSVfh5EhqbfE0mnn4nB1DCV2FOfIxGR/Ck7JYnUMJXYi2ufo2yhLyISNWWnJJEGP4kUKFvoRz2AQEQkrpSdko3OmIqUQSVeJgt74IaIVB9lp6hhKiI5GfiDkX4ZbktnLVNrp4Tab01950QkKZSd+dGlfBHJSxz6rcWhBhGRfMQht+JQw1B0xlRir5yXQeJ89Bjn2kQk/pSdh4pDbTI0NUwl9soZJMUcPZa731ASjmxFJL6Unbltl3hRw1SkQDryFhHJn7JTslEfUxERERGJBZ0xFclgau0UoLT9kiqh71Mcpj6JQw0iEkzZGSwOuRWHGoaihqnIEErZL6kS+j6V449Avn90kvKHSKSaKTsPpezMjRqmUtUyHT3GQdyPbEt5BqMS/uiIVBNlZ+GUndmpYSpVLTUEBi4/xUXcj2wrMRBFJDfKzsIpO7PT4CcRERERiQU1TEVEREQkFtQwFQlRpj5Ocen7JCISR8rO6qE+piIhinvfp6jEfbCCiERL2RmsErNTDVMRXyX+gpdTKT8v/dERSS5lZ36UndmpYSriq8Rf8HLK5fOqhEmxRSQ7/S7nR9mZnRqmIhVmd/dutu/YTt/OP9LX9UPo3Qx1h1M79uPUjnpzqLVs6QyeRmZLZy0bOjaEWouISC52dd5LV/st9PZ0UNfQyti2z9Pc8u5Qa6jmKaXUMBWpIEufW8rP7/0P2sau4axjn6S+1r801NvJ3pdu4sE//47VnVNDrOj0jI98/bZbQ6xDRGRouzrvZcvqq3D9ewDo7dnIltVXAYTeOK1WapiKVID+/n5+/cf7eeixh+jvhdNPWn6wUeqrr+3j9Lbl/P3Z10RU5aF6t4yOugQRkUN0td9yoFE6wPXvoav9FjVMQ6KGqUgFWLdxHY888Qj9PXXUvzSR5qbuwP2am7o5fkx4s8Tdn+WxMOsol2xfn4gkT29PcP/NTNul9NQwFakA+/btwzmH2zmaiYftpa9vNHV1OwbtN3z4ZG666YbQ6rr55syPhVlHudx884+iLkFESqiuoZXeno2B2yUckZ6yMLOxZnavmXWb2Toze3+WfS82sz4z251ymx9iuSKJsWPH2dTUNB6yraamkba2q0OtoyXD9CeZtktulJ0i5TG27fNYWnZaTSNj2z4fah3VvKBA1GdMvwfsA1qAE4AHzGypc255hv0fc87NC606kYTas2cW06e/gfb2m+np2UhDwyTa2q6mpeX8UOvo6NgS6vtVEWWnSBkM9CONelR+pU8JlU1kDVMzGwFcAMx0zu0GHjGz+4EPAFdFVZdUtmqaG66l5fzQG6JSfspOiUI1ZWdzy7s10ClCUV7KnwH0OedWpWxbChyf5TknmtlWM1tlZteaWcaGtZldYmZLzGxJ15auUtUsCZdtbriptVOYWjuFk1vVl0hiTdkpoVN2SliivJQ/EkgfnbEDaM6w/yJgJrAOL4AXAL1A4PAK59xtwG0Ar539WleCeqVKxHUC42xnLP7j4ZURVCQRUXZKLCUxOyvtbG8lKNsZUzP7i5m5DLdHgN3AqLSnjQJ2Bb2ec26tc+4F51y/c+4Z4AbgPeWqX6rbwBmA1FvUZwOqeSWQaqLslCRTdkqxynbG1Dk3P9vjfj+pOjOb7pxb7W+eBWTqvD/oLQArvEKR/CjEJAzKTqk0yk7JR2R9TJ1z3cBC4AYzG2Fmc4HzgLuC9jezc82sxf//scC1wH1h1SsiEgfKThGpZFEvvXIp0AhsBv4T+MTAdCdmNtWfb29gYe+zgGVm1g38Fi+YvxJBzZJg1TAHnFQFZaeEStkpYYl0HlPnXBfwrgyPrcfr5D9w/wrgipBKkwqV2tF9au2UCCsRKZyyU8Km7JSwRH3GVERyVM0rgYiIFErZmSxRr/wkkhhRh1i2aU1WrgmxEBGRPMQ5OyV+1DCVqjWhpU9z24mI5EnZKeWkhqlUraQFaKZJoj1TgDfR0LiDf/rolWGWJSJVprKy06NGdXyoj6lIQuQyF2DPntEhVCIikhy5ZKfmWo0PNUxFREREJBbUMBURERGRWFDDVERERERiQQ1TEREREYkFNUxFEiKXuQAbGneEUImISHLkkp1Rz7UqB2m6KJGEyD7B/kp+ePdt7N90GDreFBE5SNNAJYv+gomIiIhILKhhKiIiIiKxoIapiIiIiMSCGqYiIiIiEgtqmIqIiIhILKhhKiIiIiKxoIapiIiIiMSCGqYiIiIiEgtqmIqIiIhILGjlJ6lYJ7e2sqWzdtD2CS19WglERCQDZadESWdMpWIFBWu27SIiouyUaKlhKiIiIiKxoIapiIiIiMSCGqYiIiIiEgtqmIqIiIhILKhhKhVrQktfXttFRETZKdHSdFFSsTStiYhI/pSdEiWdMRURERGRWFDDVERERERiQQ1TEREREYkFNUxFREREJBbUMBURERGRWFDDVERERERiIdKGqZldZmZLzKzHzO7MYf/PmNkmM9thZneYWUMIZYqIxIqyU0QqVdRnTDuAm4A7htrRzM4BrgLOAtqAacCXy1mciEhMKTtFpCJF2jB1zi10zv0a2JbD7h8CbnfOLXfObQduBC4uZ30iSdHQ0EBDfSN1roH6eqOmJupjTiknZaeIVKokrfx0PHBfyv2lQIuZjXPODQpnM7sEuMS/u3tq7dSVIdSYi/HA1qiLiCF9LsEK/lwuvPCLJS4lNuL0s3Jk1AXkQNlZ2fS5DKbPJFicPpeM2ZmkhulIYEfK/YH/NxNw1sA5dxtwWwh15cXMljjnZkddR9zocwmmz2UwfSZ5U3ZWMH0ug+kzCZaUz6Vs1/vM7C9m5jLcHingJXcDo1LuD/x/V/HViojEg7JTRKpZ2c6YOufml/gllwOzgF/692cBnUGXokREkkrZKSLVLOrpourMbDhQC9Sa2XAzy9RY/hnwUTM7zszGANcAd4ZUainF7hJZTOhzCabPZbCq/0yUnZJCn8tg+kyCJeJzMedcdG9udj1wXdrmLzvnrjezqcBzwHHOufX+/p8FrgQagXuAjzvnekIsWUQkcspOEalUkTZMRUREREQGaLJDEREREYkFNUxFREREJBbUMI1AvutcVzIzG2tm95pZt5mtM7P3R11T1PTzMZiZNZjZ7f7PyC4z+5uZnRt1XRIu/W54lJvB9PMxWBKzM0kT7FeSgXWuz8EbjFDNvgfsA1qAE4AHzGypc255tGVFSj8fg9UBG4AzgfXAW4FfmtlrnHPtURYmodLvhke5GUw/H4MlLjs1+ClCZnYTMNk5d3HUtUTBzEYA24GZzrlV/ra7gI3OuasiLS4Gqv3nYyhmtgxvJPo9Udci4arm3w3l5tCq+ecjF3HPTl3KlyjNAPoGwtW3FG9tb5GMzKwF7+en2s8QSfVRbkrBkpCdaphKlNLX8Ma/3xxBLZIQZlYP3A381Dm3Iup6REKm3JSCJCU71TAtsTKsc13J0tfwxr+vNbwlkJnVAHfh9a+7LOJypISUnTlTbkrekpSdGvxUYmVY57qSrQLqzGy6c261v20WMb7EINExMwNuxxvw8Vbn3P6IS5ISUnbmTLkpeUladuqMaQTyXOe6YjnnuoGFwA1mNsLM5gLn4R3VVS39fGT0A+DVwDucc3uiLkbCp98N5WY2+vnIKFHZqYZpNK4B9gBXARf5/78m0oqicynetB6bgf8EPqEpT/Tzkc7MjgT+GW9qnE1mttu/XRhxaRIu/W54lJvB9PORJonZqemiRERERCQWdMZURERERGJBDVMRERERiQU1TEVEREQkFtQwFREREZFYUMNURERERGJBDVMRERERiQU1TEVEREQkFtQwFREREZFYUMNURERERGJBDVOpOmbWaGYvmtl6M2tIe+zHZtZnZv8YVX0iInGk7JQwqGEqVcc5twe4DpiCt+Y0AGZ2M/BR4HLn3C8iKk9EJJaUnRIGc85FXYNI6MysFlgKHA5MAz4G3Apc55y7IcraRETiStkp5aaGqVQtM3s78N/Ag8Abge865/4l2qpEROJN2SnlpEv5UrWcc78BngLOAhYAn0rfx8w+aWaPm9leM/tLyCWKiMSOslPKqS7qAkSiYmbvA07w7+5ywZcPXgK+CpwCvC6s2kRE4krZKeWkhqlUJTN7M3AXcC+wH/iImd3qnPt76n7OuYX+/lPDr1JEJF6UnVJuupQvVcfM5gALgUeBC4FrgH7g5ijrEhGJM2WnhEENU6kqZvZq4AFgFfAu51yPc24NcDtwnpnNjbRAEZEYUnZKWNQwlarhX1L6I7ADONc5tzPl4RuAPcAtUdQmIhJXyk4Jk/qYStVwzq3Hmxg66LGXgKZwKxIRiT9lp4RJDVORLMysDu/3pA6oMbPhQL9zbl+0lYmIxJeyUwqlhqlIdtfgLcE3YA/wEDA/kmpERJJB2SkF0cpPIiIiIhILGvwkIiIiIrGghqmIiIiIxIIapiIiIiISC2qYioiIiEgsqGEqIiIiIrGghqmIiIiIxIIapiIiIiISC/8/llFWAs/2i1QAAAAASUVORK5CYII=\n",
- "text/plain": [
- ""
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
+ "execution_count": 9,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
"source": [
"from __future__ import division, print_function, unicode_literals\n",
"\n",
@@ -1434,22 +1175,11 @@
},
{
"cell_type": "code",
- "execution_count": 8,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- ""
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
+ "execution_count": 10,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
"source": [
"np.random.seed(6)\n",
"Xs = np.random.rand(100, 2) - 0.5\n",
@@ -1482,8 +1212,10 @@
},
{
"cell_type": "code",
- "execution_count": 9,
- "metadata": {},
+ "execution_count": 11,
+ "metadata": {
+ "collapsed": false
+ },
"outputs": [],
"source": [
"# Quadratic training set + noise\n",
@@ -1496,20 +1228,11 @@
},
{
"cell_type": "code",
- "execution_count": 10,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "DecisionTreeRegressor(max_depth=2, random_state=42)"
- ]
- },
- "execution_count": 10,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "execution_count": 12,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
"source": [
"from sklearn.tree import DecisionTreeRegressor\n",
"\n",
@@ -1526,22 +1249,11 @@
},
{
"cell_type": "code",
- "execution_count": 11,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- ""
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
+ "execution_count": 13,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
"source": [
"from sklearn.tree import DecisionTreeRegressor\n",
"\n",
@@ -1585,22 +1297,11 @@
},
{
"cell_type": "code",
- "execution_count": 12,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- ""
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
+ "execution_count": 14,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
"source": [
"tree_reg1 = DecisionTreeRegressor(random_state=42)\n",
"tree_reg2 = DecisionTreeRegressor(random_state=42, min_samples_leaf=10)\n",
@@ -1754,22 +1455,11 @@
},
{
"cell_type": "code",
- "execution_count": 13,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- ""
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
+ "execution_count": 15,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
"source": [
"heads_proba = 0.51\n",
"coin_tosses = (np.random.rand(10000, 10) < heads_proba).astype(np.int32)\n",
@@ -1795,24 +1485,11 @@
},
{
"cell_type": "code",
- "execution_count": 14,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "LogisticRegression 0.864\n",
- "RandomForestClassifier 0.872\n",
- "SVC 0.888\n",
- "VotingClassifier 0.896\n",
- "LogisticRegression 0.864\n",
- "RandomForestClassifier 0.872\n",
- "SVC 0.888\n",
- "VotingClassifier 0.912\n"
- ]
- }
- ],
+ "execution_count": 16,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
"source": [
"from sklearn.model_selection import train_test_split\n",
"from sklearn.datasets import make_moons\n",
@@ -1868,22 +1545,11 @@
},
{
"cell_type": "code",
- "execution_count": 15,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "VotingClassifier(estimators=[('lr', LogisticRegression(random_state=42)),\n",
- " ('rf', RandomForestClassifier(random_state=42)),\n",
- " ('svc', SVC(random_state=42))])"
- ]
- },
- "execution_count": 15,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "execution_count": 17,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
"source": [
"from sklearn.model_selection import train_test_split\n",
"from sklearn.datasets import make_moons\n",
@@ -1907,20 +1573,11 @@
},
{
"cell_type": "code",
- "execution_count": 16,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "LogisticRegression 0.864\n",
- "RandomForestClassifier 0.896\n",
- "SVC 0.896\n",
- "VotingClassifier 0.912\n"
- ]
- }
- ],
+ "execution_count": 18,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
"source": [
"from sklearn.metrics import accuracy_score\n",
"\n",
@@ -1932,23 +1589,11 @@
},
{
"cell_type": "code",
- "execution_count": 17,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "VotingClassifier(estimators=[('lr', LogisticRegression(random_state=42)),\n",
- " ('rf', RandomForestClassifier(random_state=42)),\n",
- " ('svc', SVC(probability=True, random_state=42))],\n",
- " voting='soft')"
- ]
- },
- "execution_count": 17,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "execution_count": 19,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
"source": [
"log_clf = LogisticRegression(random_state=42)\n",
"rnd_clf = RandomForestClassifier(random_state=42)\n",
@@ -1962,20 +1607,11 @@
},
{
"cell_type": "code",
- "execution_count": 18,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "LogisticRegression 0.864\n",
- "RandomForestClassifier 0.896\n",
- "SVC 0.896\n",
- "VotingClassifier 0.92\n"
- ]
- }
- ],
+ "execution_count": 20,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
"source": [
"from sklearn.metrics import accuracy_score\n",
"\n",
@@ -1994,8 +1630,10 @@
},
{
"cell_type": "code",
- "execution_count": 19,
- "metadata": {},
+ "execution_count": 21,
+ "metadata": {
+ "collapsed": false
+ },
"outputs": [],
"source": [
"from sklearn.ensemble import BaggingClassifier\n",
@@ -2010,17 +1648,11 @@
},
{
"cell_type": "code",
- "execution_count": 20,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "0.904\n"
- ]
- }
- ],
+ "execution_count": 22,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
"source": [
"from sklearn.metrics import accuracy_score\n",
"print(accuracy_score(y_test, y_pred))"
@@ -2028,17 +1660,11 @@
},
{
"cell_type": "code",
- "execution_count": 21,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "0.856\n"
- ]
- }
- ],
+ "execution_count": 23,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
"source": [
"tree_clf = DecisionTreeClassifier(random_state=42)\n",
"tree_clf.fit(X_train, y_train)\n",
@@ -2048,22 +1674,11 @@
},
{
"cell_type": "code",
- "execution_count": 22,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- ""
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
+ "execution_count": 24,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
"source": [
"from matplotlib.colors import ListedColormap\n",
"\n",
@@ -2106,8 +1721,10 @@
},
{
"cell_type": "code",
- "execution_count": 23,
- "metadata": {},
+ "execution_count": 25,
+ "metadata": {
+ "collapsed": false
+ },
"outputs": [],
"source": [
"\n",
@@ -2171,25 +1788,7 @@
]
}
],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.6.8"
- }
- },
+ "metadata": {},
"nbformat": 4,
"nbformat_minor": 4
}
diff --git a/doc/src/week44/Programs/simpletree.py b/doc/src/week44/Programs/simpletree.py
new file mode 100644
index 000000000..8c1d097ea
--- /dev/null
+++ b/doc/src/week44/Programs/simpletree.py
@@ -0,0 +1,7 @@
+from sklearn.datasets import load_iris
+from sklearn import tree
+X, y = load_iris(return_X_y=True)
+tree_clf = tree.DecisionTreeClassifier()
+tree_clf = tree_clf.fit(X, y)
+# and then plot the tree
+tree.plot_tree(tree_clf)
diff --git a/doc/src/week44/week44.do.txt b/doc/src/week44/week44.do.txt
index e65d62d8c..dca6407da 100644
--- a/doc/src/week44/week44.do.txt
+++ b/doc/src/week44/week44.do.txt
@@ -6,7 +6,7 @@ DATE: today
!split
===== Overview of week 44 =====
-* Thursday: Wrapping up PCA from last week and basics of decision trees, classification and regression algorithms
+* "Thursday: Wrapping up PCA from last week and basics of decision trees, classification and regression algorithms with video of lecture":"https://www.uio.no/studier/emner/matnat/fys/FYS-STK4155/h20/forelesningsvideoer/LectureOctober29.mp4?vrtx=view-as-webpage"
* Friday: Decision trees, voting models and bagging
@@ -16,7 +16,7 @@ Geron's chapter 6 covers decision trees while ensemble models, voting and baggin
!split
===== Thursday =====
-Overview video, aims and motivations.
+
!split
===== Decision trees, overarching aims =====
@@ -313,8 +313,11 @@ way to do just this. Rather than considering every possible subtree,
we consider a sequence of trees indexed by a nonnegative tuning
parameter $\alpha$.
+Read more at the following "Scikit-Learn link on pruning":"https://scikit-learn.org/stable/auto_examples/tree/plot_cost_complexity_pruning.html#sphx-glr-auto-examples-tree-plot-cost-complexity-pruning-py".
+
!split
===== Cost complexity pruning =====
+
For each value of $\alpha$ there corresponds a subtree $T \in T_0$ such that
!bt
\[
@@ -326,7 +329,7 @@ the number of terminal nodes of the tree $T$ , $R_m$ is the
rectangle (i.e. the subset of predictor space) corresponding to the $m$-th terminal node.
The tuning parameter $\alpha$ controls a trade-off between the subtree’s
-com- plexity and its fit to the training data. When $\alpha = 0$, then the
+complexity and its fit to the training data. When $\alpha = 0$, then the
subtree $T$ will simply equal $T_0$,
because then the above equation just measures the
training error.
@@ -347,6 +350,7 @@ subtree corresponding to $\alpha$.
===== Schematic Regression Procedure =====
!bblock Building a Regression Tree
+
o Use recursive binary splitting to grow a large tree on the training data, stopping only when each terminal node has fewer than some minimum number of observations.
o Apply cost complexity pruning to the large tree in order to obtain a sequence of best subtrees, as a function of $\alpha$.
o Use for example $K$-fold cross-validation to choose $\alpha$. Divide the training observations into $K$ folds. For each $k=1,2,\dots,K$ we:
@@ -502,6 +506,38 @@ cmd = 'dot -Tpng DataFiles/moons.dot -o DataFiles/moons.png'
os.system(cmd)
!ec
+!split
+===== Other ways of visualizing the trees =====
+
+_Scikit-Learn_ has also another way to visualize the trees which is very useful, here with the Iris data.
+
+!bc pycod
+from sklearn.datasets import load_iris
+from sklearn import tree
+X, y = load_iris(return_X_y=True)
+tree_clf = tree.DecisionTreeClassifier()
+tree_clf = tree_clf.fit(X, y)
+# and then plot the tree
+tree.plot_tree(tree_clf)
+!ec
+
+!split
+===== Printing out as text =====
+
+Alternatively, the tree can also be exported in textual format with the function exporttext.
+This method doesn’t require the installation of external libraries and is more compact:
+
+!bc pycod
+from sklearn.datasets import load_iris
+from sklearn.tree import DecisionTreeClassifier
+from sklearn.tree import export_text
+iris = load_iris()
+decision_tree = DecisionTreeClassifier(random_state=0, max_depth=2)
+decision_tree = decision_tree.fit(iris.data, iris.target)
+r = export_text(decision_tree, feature_names=iris['feature_names'])
+print(r)
+!ec
+
!split
===== Algorithms for Setting up Decision Trees =====
