From b87fd2d131dc65be10e1bdb2c47b174623b3d5ef Mon Sep 17 00:00:00 2001 From: mhjensen Date: Fri, 8 Nov 2019 06:43:17 +0100 Subject: [PATCH] added html --- .../html/._DecisionTrees-bs058.html | 324 ++++++++++++++++ .../html/._DecisionTrees-bs059.html | 357 ++++++++++++++++++ .../html/._DecisionTrees-bs060.html | 355 +++++++++++++++++ 3 files changed, 1036 insertions(+) create mode 100644 doc/pub/DecisionTrees/html/._DecisionTrees-bs058.html create mode 100644 doc/pub/DecisionTrees/html/._DecisionTrees-bs059.html create mode 100644 doc/pub/DecisionTrees/html/._DecisionTrees-bs060.html diff --git a/doc/pub/DecisionTrees/html/._DecisionTrees-bs058.html b/doc/pub/DecisionTrees/html/._DecisionTrees-bs058.html new file mode 100644 index 000000000..318bc2a43 --- /dev/null +++ b/doc/pub/DecisionTrees/html/._DecisionTrees-bs058.html @@ -0,0 +1,324 @@ + + + + + + + + +Data Analysis and Machine Learning: From Decision Trees to Forests and all that + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + Data Analysis and Machine Learning: From Decision Trees to Forests and all that +
+ +
+ +
+
+ + +
+ +

 

 

 

+ + + + +

XGBoost: Extreme Gradient Boosting

+ +

+XGBoost or Extreme Gradient +Boosting, is an optimized distributed gradient boosting library +designed to be highly efficient, flexible and portable. It implements +machine learning algorithms under the Gradient Boosting +framework. XGBoost provides a parallel tree boosting that solve many +data science problems in a fast and accurate way. See the article by Chen and Guestrin. + +

+The authors design and build a highly scalable end-to-end tree +boosting system. It has a theoretically justified weighted quantile +sketch for efficient proposal calculation. It introduces a novel sparsity-aware algorithm for parallel tree learning and an effective cache-aware block structure for out-of-core tree learning. + +

+It is now the algorithm which wins essentially all ML competitions!!! + +

+

+ +

+ + +
+ + + + + + + +
+ +
+ + + + + + diff --git a/doc/pub/DecisionTrees/html/._DecisionTrees-bs059.html b/doc/pub/DecisionTrees/html/._DecisionTrees-bs059.html new file mode 100644 index 000000000..78f4557e4 --- /dev/null +++ b/doc/pub/DecisionTrees/html/._DecisionTrees-bs059.html @@ -0,0 +1,357 @@ + + + + + + + + +Data Analysis and Machine Learning: From Decision Trees to Forests and all that + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + Data Analysis and Machine Learning: From Decision Trees to Forests and all that +
+ +
+ +
+
+ + +
+ +

 

 

 

+ + + + +

Regression Case

+ +

+ + +

import matplotlib.pyplot as plt
+import numpy as np
+from sklearn.model_selection import train_test_split
+import xgboost as xgb
+from sklearn.preprocessing import StandardScaler
+import scikitplot as skplt
+from sklearn.metrics import mean_squared_error
+
+n = 100
+maxdegree = 6
+
+# Make data set.
+x = np.linspace(-3, 3, n).reshape(-1, 1)
+y = np.exp(-x**2) + 1.5 * np.exp(-(x-2)**2)+ np.random.normal(0, 0.1, x.shape)
+
+error = np.zeros(maxdegree)
+bias = np.zeros(maxdegree)
+variance = np.zeros(maxdegree)
+polydegree = np.zeros(maxdegree)
+X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.2)
+scaler = StandardScaler()
+scaler.fit(X_train)
+X_train_scaled = scaler.transform(X_train)
+X_test_scaled = scaler.transform(X_test)
+
+for degree in range(maxdegree):
+    model =  xgb.XGBRegressor(objective ='reg:squarederror', colsaobjective ='reg:squarederror', colsample_bytree = 0.3, learning_rate = 0.1,max_depth = degree, alpha = 10, n_estimators = 200)
+
+    model.fit(X_train_scaled,y_train)
+    y_pred = model.predict(X_test_scaled)
+    polydegree[degree] = degree
+    error[degree] = np.mean( np.mean((y_test - y_pred)**2) )
+    bias[degree] = np.mean( (y_test - np.mean(y_pred))**2 )
+    variance[degree] = np.mean( np.var(y_pred) )
+    print('Max depth:', degree)
+    print('Error:', error[degree])
+    print('Bias^2:', bias[degree])
+    print('Var:', variance[degree])
+    print('{} >= {} + {} = {}'.format(error[degree], bias[degree], variance[degree], bias[degree]+variance[degree]))
+
+plt.xlim(1,maxdegree-1)
+plt.plot(polydegree, error, label='Error')
+plt.plot(polydegree, bias, label='bias')
+plt.plot(polydegree, variance, label='Variance')
+plt.legend()
+plt.show()
+
+

+

+ +

+ + +
+ + + + + + + +
+ +
+ + + + + + diff --git a/doc/pub/DecisionTrees/html/._DecisionTrees-bs060.html b/doc/pub/DecisionTrees/html/._DecisionTrees-bs060.html new file mode 100644 index 000000000..91925956f --- /dev/null +++ b/doc/pub/DecisionTrees/html/._DecisionTrees-bs060.html @@ -0,0 +1,355 @@ + + + + + + + + +Data Analysis and Machine Learning: From Decision Trees to Forests and all that + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + Data Analysis and Machine Learning: From Decision Trees to Forests and all that +
+ +
+ +
+
+ + +
+ +

 

 

 

+ + + + +

Xgboost on the Cancer Data

+

+ + +

import matplotlib.pyplot as plt
+import numpy as np
+from sklearn.model_selection import  train_test_split 
+from sklearn.datasets import load_breast_cancer
+from sklearn.preprocessing import LabelEncoder
+from sklearn.model_selection import cross_validate
+import scikitplot as skplt
+import xgboost as xgb
+# Load the data
+cancer = load_breast_cancer()
+
+X_train, X_test, y_train, y_test = train_test_split(cancer.data,cancer.target,random_state=0)
+print(X_train.shape)
+print(X_test.shape)
+#now scale the data
+from sklearn.preprocessing import StandardScaler
+scaler = StandardScaler()
+scaler.fit(X_train)
+X_train_scaled = scaler.transform(X_train)
+X_test_scaled = scaler.transform(X_test)
+
+xg_clf = xgb.XGBClassifier()
+xg_clf.fit(X_train_scaled,y_train)
+
+y_test = xg_clf.predict(X_test_scaled)
+
+print("Test set accuracy with Random Forests and scaled data: {:.2f}".format(xg_clf.score(X_test_scaled,y_test)))
+
+import scikitplot as skplt
+y_pred = xg_clf.predict(X_test_scaled)
+skplt.metrics.plot_confusion_matrix(y_test, y_pred, normalize=True)
+plt.show()
+y_probas = xg_clf.predict_proba(X_test_scaled)
+skplt.metrics.plot_roc(y_test, y_probas)
+plt.show()
+skplt.metrics.plot_cumulative_gain(y_test, y_probas)
+plt.show()
+
+
+xgb.plot_tree(xg_clf,num_trees=0)
+plt.rcParams['figure.figsize'] = [50, 10]
+plt.show()
+
+xgb.plot_importance(xg_clf)
+plt.rcParams['figure.figsize'] = [5, 5]
+plt.show()
+
+

+ +

+ +

+ + +
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