diff --git a/doc/pub/week38/html/._week38-bs000.html b/doc/pub/week38/html/._week38-bs000.html index 6d00cb4a2..5a7692ec7 100644 --- a/doc/pub/week38/html/._week38-bs000.html +++ b/doc/pub/week38/html/._week38-bs000.html @@ -62,7 +62,15 @@ Automatically generated HTML file from DocOnce source ('Extending to more predictors', 2, None, '___sec14'), ('Including more classes', 2, None, '___sec15'), ('More classes', 2, None, '___sec16'), - ('A simple classification problem', 2, None, '___sec17')]} + ('A simple classification problem', 2, None, '___sec17'), + ('Cancer Data again now with Decision Trees and other Methods', + 2, + None, + '___sec18'), + ('Other measures in classification studies: Cancer Data again', + 2, + None, + '___sec19')]} end of tocinfo --> @@ -118,6 +126,8 @@ MathJax.Hub.Config({
  • Including more classes
  • More classes
  • A simple classification problem
    • +
  • Cancer Data again now with Decision Trees and other Methods
    • +
  • Other measures in classification studies: Cancer Data again
    • @@ -176,7 +186,7 @@ MathJax.Hub.Config({
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    • diff --git a/doc/pub/week38/html/._week38-bs001.html b/doc/pub/week38/html/._week38-bs001.html index 11627073b..f9048fb47 100644 --- a/doc/pub/week38/html/._week38-bs001.html +++ b/doc/pub/week38/html/._week38-bs001.html @@ -62,7 +62,15 @@ Automatically generated HTML file from DocOnce source ('Extending to more predictors', 2, None, '___sec14'), ('Including more classes', 2, None, '___sec15'), ('More classes', 2, None, '___sec16'), - ('A simple classification problem', 2, None, '___sec17')]} + ('A simple classification problem', 2, None, '___sec17'), + ('Cancer Data again now with Decision Trees and other Methods', + 2, + None, + '___sec18'), + ('Other measures in classification studies: Cancer Data again', + 2, + None, + '___sec19')]} end of tocinfo --> @@ -118,6 +126,8 @@ MathJax.Hub.Config({
  • Including more classes
  • More classes
  • A simple classification problem
    • +
  • Cancer Data again now with Decision Trees and other Methods
    • +
  • Other measures in classification studies: Cancer Data again
    • @@ -158,7 +168,7 @@ MathJax.Hub.Config({
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    • diff --git a/doc/pub/week38/html/._week38-bs002.html b/doc/pub/week38/html/._week38-bs002.html index 2a7360d9c..263695cc0 100644 --- a/doc/pub/week38/html/._week38-bs002.html +++ b/doc/pub/week38/html/._week38-bs002.html @@ -62,7 +62,15 @@ Automatically generated HTML file from DocOnce source ('Extending to more predictors', 2, None, '___sec14'), ('Including more classes', 2, None, '___sec15'), ('More classes', 2, None, '___sec16'), - ('A simple classification problem', 2, None, '___sec17')]} + ('A simple classification problem', 2, None, '___sec17'), + ('Cancer Data again now with Decision Trees and other Methods', + 2, + None, + '___sec18'), + ('Other measures in classification studies: Cancer Data again', + 2, + None, + '___sec19')]} end of tocinfo --> @@ -118,6 +126,8 @@ MathJax.Hub.Config({
  • Including more classes
  • More classes
  • A simple classification problem
    • +
  • Cancer Data again now with Decision Trees and other Methods
    • +
  • Other measures in classification studies: Cancer Data again
    • @@ -166,7 +176,7 @@ simple recipe for fitting our data.
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    • diff --git a/doc/pub/week38/html/._week38-bs003.html b/doc/pub/week38/html/._week38-bs003.html index 7a20afd9e..1d6f601ad 100644 --- a/doc/pub/week38/html/._week38-bs003.html +++ b/doc/pub/week38/html/._week38-bs003.html @@ -62,7 +62,15 @@ Automatically generated HTML file from DocOnce source ('Extending to more predictors', 2, None, '___sec14'), ('Including more classes', 2, None, '___sec15'), ('More classes', 2, None, '___sec16'), - ('A simple classification problem', 2, None, '___sec17')]} + ('A simple classification problem', 2, None, '___sec17'), + ('Cancer Data again now with Decision Trees and other Methods', + 2, + None, + '___sec18'), + ('Other measures in classification studies: Cancer Data again', + 2, + None, + '___sec19')]} end of tocinfo --> @@ -118,6 +126,8 @@ MathJax.Hub.Config({
  • Including more classes
  • More classes
  • A simple classification problem
    • +
  • Cancer Data again now with Decision Trees and other Methods
    • +
  • Other measures in classification studies: Cancer Data again
    • @@ -172,7 +182,7 @@ failure etc.
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    • diff --git a/doc/pub/week38/html/._week38-bs004.html b/doc/pub/week38/html/._week38-bs004.html index 298c19e6e..cb70b668a 100644 --- a/doc/pub/week38/html/._week38-bs004.html +++ b/doc/pub/week38/html/._week38-bs004.html @@ -62,7 +62,15 @@ Automatically generated HTML file from DocOnce source ('Extending to more predictors', 2, None, '___sec14'), ('Including more classes', 2, None, '___sec15'), ('More classes', 2, None, '___sec16'), - ('A simple classification problem', 2, None, '___sec17')]} + ('A simple classification problem', 2, None, '___sec17'), + ('Cancer Data again now with Decision Trees and other Methods', + 2, + None, + '___sec18'), + ('Other measures in classification studies: Cancer Data again', + 2, + None, + '___sec19')]} end of tocinfo --> @@ -118,6 +126,8 @@ MathJax.Hub.Config({
  • Including more classes
  • More classes
  • A simple classification problem
    • +
  • Cancer Data again now with Decision Trees and other Methods
    • +
  • Other measures in classification studies: Cancer Data again
    • @@ -171,7 +181,7 @@ models, as we will see later.
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    • diff --git a/doc/pub/week38/html/._week38-bs005.html b/doc/pub/week38/html/._week38-bs005.html index 0625d52da..5987788c5 100644 --- a/doc/pub/week38/html/._week38-bs005.html +++ b/doc/pub/week38/html/._week38-bs005.html @@ -62,7 +62,15 @@ Automatically generated HTML file from DocOnce source ('Extending to more predictors', 2, None, '___sec14'), ('Including more classes', 2, None, '___sec15'), ('More classes', 2, None, '___sec16'), - ('A simple classification problem', 2, None, '___sec17')]} + ('A simple classification problem', 2, None, '___sec17'), + ('Cancer Data again now with Decision Trees and other Methods', + 2, + None, + '___sec18'), + ('Other measures in classification studies: Cancer Data again', + 2, + None, + '___sec19')]} end of tocinfo --> @@ -118,6 +126,8 @@ MathJax.Hub.Config({
  • Including more classes
  • More classes
  • A simple classification problem
    • +
  • Cancer Data again now with Decision Trees and other Methods
    • +
  • Other measures in classification studies: Cancer Data again
    • @@ -178,7 +188,7 @@ $$
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    • diff --git a/doc/pub/week38/html/._week38-bs006.html b/doc/pub/week38/html/._week38-bs006.html index c3742d046..ceaa89664 100644 --- a/doc/pub/week38/html/._week38-bs006.html +++ b/doc/pub/week38/html/._week38-bs006.html @@ -62,7 +62,15 @@ Automatically generated HTML file from DocOnce source ('Extending to more predictors', 2, None, '___sec14'), ('Including more classes', 2, None, '___sec15'), ('More classes', 2, None, '___sec16'), - ('A simple classification problem', 2, None, '___sec17')]} + ('A simple classification problem', 2, None, '___sec17'), + ('Cancer Data again now with Decision Trees and other Methods', + 2, + None, + '___sec18'), + ('Other measures in classification studies: Cancer Data again', + 2, + None, + '___sec19')]} end of tocinfo --> @@ -118,6 +126,8 @@ MathJax.Hub.Config({
  • Including more classes
  • More classes
  • A simple classification problem
    • +
  • Cancer Data again now with Decision Trees and other Methods
    • +
  • Other measures in classification studies: Cancer Data again
    • @@ -176,7 +186,7 @@ where \( \hat{y} \) is a vector representing the possible outcomes, \( \hat{X} \
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    • diff --git a/doc/pub/week38/html/._week38-bs007.html b/doc/pub/week38/html/._week38-bs007.html index 5c4d49656..c6414f52b 100644 --- a/doc/pub/week38/html/._week38-bs007.html +++ b/doc/pub/week38/html/._week38-bs007.html @@ -62,7 +62,15 @@ Automatically generated HTML file from DocOnce source ('Extending to more predictors', 2, None, '___sec14'), ('Including more classes', 2, None, '___sec15'), ('More classes', 2, None, '___sec16'), - ('A simple classification problem', 2, None, '___sec17')]} + ('A simple classification problem', 2, None, '___sec17'), + ('Cancer Data again now with Decision Trees and other Methods', + 2, + None, + '___sec18'), + ('Other measures in classification studies: Cancer Data again', + 2, + None, + '___sec19')]} end of tocinfo --> @@ -118,6 +126,8 @@ MathJax.Hub.Config({
  • Including more classes
  • More classes
  • A simple classification problem
    • +
  • Cancer Data again now with Decision Trees and other Methods
    • +
  • Other measures in classification studies: Cancer Data again
    • @@ -175,7 +185,7 @@ the probability of a given category. This leads us to the logistic function.
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    • diff --git a/doc/pub/week38/html/._week38-bs008.html b/doc/pub/week38/html/._week38-bs008.html index 35d593d40..514250305 100644 --- a/doc/pub/week38/html/._week38-bs008.html +++ b/doc/pub/week38/html/._week38-bs008.html @@ -62,7 +62,15 @@ Automatically generated HTML file from DocOnce source ('Extending to more predictors', 2, None, '___sec14'), ('Including more classes', 2, None, '___sec15'), ('More classes', 2, None, '___sec16'), - ('A simple classification problem', 2, None, '___sec17')]} + ('A simple classification problem', 2, None, '___sec17'), + ('Cancer Data again now with Decision Trees and other Methods', + 2, + None, + '___sec18'), + ('Other measures in classification studies: Cancer Data again', + 2, + None, + '___sec19')]} end of tocinfo --> @@ -118,6 +126,8 @@ MathJax.Hub.Config({
  • Including more classes
  • More classes
  • A simple classification problem
    • +
  • Cancer Data again now with Decision Trees and other Methods
    • +
  • Other measures in classification studies: Cancer Data again
    • @@ -176,7 +186,7 @@ Note that \( 1-p(t)= p(-t) \).
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    • diff --git a/doc/pub/week38/html/._week38-bs009.html b/doc/pub/week38/html/._week38-bs009.html index fbb3a245b..5a99f8674 100644 --- a/doc/pub/week38/html/._week38-bs009.html +++ b/doc/pub/week38/html/._week38-bs009.html @@ -62,7 +62,15 @@ Automatically generated HTML file from DocOnce source ('Extending to more predictors', 2, None, '___sec14'), ('Including more classes', 2, None, '___sec15'), ('More classes', 2, None, '___sec16'), - ('A simple classification problem', 2, None, '___sec17')]} + ('A simple classification problem', 2, None, '___sec17'), + ('Cancer Data again now with Decision Trees and other Methods', + 2, + None, + '___sec18'), + ('Other measures in classification studies: Cancer Data again', + 2, + None, + '___sec19')]} end of tocinfo --> @@ -118,6 +126,8 @@ MathJax.Hub.Config({
  • Including more classes
  • More classes
  • A simple classification problem
    • +
  • Cancer Data again now with Decision Trees and other Methods
    • +
  • Other measures in classification studies: Cancer Data again
    • @@ -220,6 +230,8 @@ plt.show()
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    • diff --git a/doc/pub/week38/html/._week38-bs010.html b/doc/pub/week38/html/._week38-bs010.html index 37598fe3a..23eee0202 100644 --- a/doc/pub/week38/html/._week38-bs010.html +++ b/doc/pub/week38/html/._week38-bs010.html @@ -62,7 +62,15 @@ Automatically generated HTML file from DocOnce source ('Extending to more predictors', 2, None, '___sec14'), ('Including more classes', 2, None, '___sec15'), ('More classes', 2, None, '___sec16'), - ('A simple classification problem', 2, None, '___sec17')]} + ('A simple classification problem', 2, None, '___sec17'), + ('Cancer Data again now with Decision Trees and other Methods', + 2, + None, + '___sec18'), + ('Other measures in classification studies: Cancer Data again', + 2, + None, + '___sec19')]} end of tocinfo --> @@ -118,6 +126,8 @@ MathJax.Hub.Config({
  • Including more classes
  • More classes
  • A simple classification problem
    • +
  • Cancer Data again now with Decision Trees and other Methods
    • +
  • Other measures in classification studies: Cancer Data again
    • @@ -176,6 +186,9 @@ $$
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    • diff --git a/doc/pub/week38/html/._week38-bs011.html b/doc/pub/week38/html/._week38-bs011.html index 16b319b9a..9b25ba49a 100644 --- a/doc/pub/week38/html/._week38-bs011.html +++ b/doc/pub/week38/html/._week38-bs011.html @@ -62,7 +62,15 @@ Automatically generated HTML file from DocOnce source ('Extending to more predictors', 2, None, '___sec14'), ('Including more classes', 2, None, '___sec15'), ('More classes', 2, None, '___sec16'), - ('A simple classification problem', 2, None, '___sec17')]} + ('A simple classification problem', 2, None, '___sec17'), + ('Cancer Data again now with Decision Trees and other Methods', + 2, + None, + '___sec18'), + ('Other measures in classification studies: Cancer Data again', + 2, + None, + '___sec19')]} end of tocinfo --> @@ -118,6 +126,8 @@ MathJax.Hub.Config({
  • Including more classes
  • More classes
  • A simple classification problem
    • +
  • Cancer Data again now with Decision Trees and other Methods
    • +
  • Other measures in classification studies: Cancer Data again
    • @@ -176,6 +186,8 @@ $$
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    • diff --git a/doc/pub/week38/html/._week38-bs012.html b/doc/pub/week38/html/._week38-bs012.html index cadf8fee8..e29e39927 100644 --- a/doc/pub/week38/html/._week38-bs012.html +++ b/doc/pub/week38/html/._week38-bs012.html @@ -62,7 +62,15 @@ Automatically generated HTML file from DocOnce source ('Extending to more predictors', 2, None, '___sec14'), ('Including more classes', 2, None, '___sec15'), ('More classes', 2, None, '___sec16'), - ('A simple classification problem', 2, None, '___sec17')]} + ('A simple classification problem', 2, None, '___sec17'), + ('Cancer Data again now with Decision Trees and other Methods', + 2, + None, + '___sec18'), + ('Other measures in classification studies: Cancer Data again', + 2, + None, + '___sec19')]} end of tocinfo --> @@ -118,6 +126,8 @@ MathJax.Hub.Config({
  • Including more classes
  • More classes
  • A simple classification problem
    • +
  • Cancer Data again now with Decision Trees and other Methods
    • +
  • Other measures in classification studies: Cancer Data again
    • @@ -173,6 +183,8 @@ in practice we often supplement the cross-entropy with additional regularization
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    • diff --git a/doc/pub/week38/html/._week38-bs013.html b/doc/pub/week38/html/._week38-bs013.html index abe1ad6a1..5bea6dae7 100644 --- a/doc/pub/week38/html/._week38-bs013.html +++ b/doc/pub/week38/html/._week38-bs013.html @@ -62,7 +62,15 @@ Automatically generated HTML file from DocOnce source ('Extending to more predictors', 2, None, '___sec14'), ('Including more classes', 2, None, '___sec15'), ('More classes', 2, None, '___sec16'), - ('A simple classification problem', 2, None, '___sec17')]} + ('A simple classification problem', 2, None, '___sec17'), + ('Cancer Data again now with Decision Trees and other Methods', + 2, + None, + '___sec18'), + ('Other measures in classification studies: Cancer Data again', + 2, + None, + '___sec19')]} end of tocinfo --> @@ -118,6 +126,8 @@ MathJax.Hub.Config({
  • Including more classes
  • More classes
  • A simple classification problem
    • +
  • Cancer Data again now with Decision Trees and other Methods
    • +
  • Other measures in classification studies: Cancer Data again
    • @@ -173,6 +183,8 @@ $$
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    • diff --git a/doc/pub/week38/html/._week38-bs014.html b/doc/pub/week38/html/._week38-bs014.html index eb7603a2b..21ebd89b7 100644 --- a/doc/pub/week38/html/._week38-bs014.html +++ b/doc/pub/week38/html/._week38-bs014.html @@ -62,7 +62,15 @@ Automatically generated HTML file from DocOnce source ('Extending to more predictors', 2, None, '___sec14'), ('Including more classes', 2, None, '___sec15'), ('More classes', 2, None, '___sec16'), - ('A simple classification problem', 2, None, '___sec17')]} + ('A simple classification problem', 2, None, '___sec17'), + ('Cancer Data again now with Decision Trees and other Methods', + 2, + None, + '___sec18'), + ('Other measures in classification studies: Cancer Data again', + 2, + None, + '___sec19')]} end of tocinfo --> @@ -118,6 +126,8 @@ MathJax.Hub.Config({
  • Including more classes
  • More classes
  • A simple classification problem
    • +
  • Cancer Data again now with Decision Trees and other Methods
    • +
  • Other measures in classification studies: Cancer Data again
    • @@ -173,6 +183,8 @@ $$
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    • diff --git a/doc/pub/week38/html/._week38-bs015.html b/doc/pub/week38/html/._week38-bs015.html index 89c9ea022..90ac81e95 100644 --- a/doc/pub/week38/html/._week38-bs015.html +++ b/doc/pub/week38/html/._week38-bs015.html @@ -62,7 +62,15 @@ Automatically generated HTML file from DocOnce source ('Extending to more predictors', 2, None, '___sec14'), ('Including more classes', 2, None, '___sec15'), ('More classes', 2, None, '___sec16'), - ('A simple classification problem', 2, None, '___sec17')]} + ('A simple classification problem', 2, None, '___sec17'), + ('Cancer Data again now with Decision Trees and other Methods', + 2, + None, + '___sec18'), + ('Other measures in classification studies: Cancer Data again', + 2, + None, + '___sec19')]} end of tocinfo --> @@ -118,6 +126,8 @@ MathJax.Hub.Config({
  • Including more classes
  • More classes
  • A simple classification problem
    • +
  • Cancer Data again now with Decision Trees and other Methods
    • +
  • Other measures in classification studies: Cancer Data again
    • @@ -165,6 +175,8 @@ $$
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    • diff --git a/doc/pub/week38/html/._week38-bs016.html b/doc/pub/week38/html/._week38-bs016.html index 9b51accbd..5885f44ce 100644 --- a/doc/pub/week38/html/._week38-bs016.html +++ b/doc/pub/week38/html/._week38-bs016.html @@ -62,7 +62,15 @@ Automatically generated HTML file from DocOnce source ('Extending to more predictors', 2, None, '___sec14'), ('Including more classes', 2, None, '___sec15'), ('More classes', 2, None, '___sec16'), - ('A simple classification problem', 2, None, '___sec17')]} + ('A simple classification problem', 2, None, '___sec17'), + ('Cancer Data again now with Decision Trees and other Methods', + 2, + None, + '___sec18'), + ('Other measures in classification studies: Cancer Data again', + 2, + None, + '___sec19')]} end of tocinfo --> @@ -118,6 +126,8 @@ MathJax.Hub.Config({
  • Including more classes
  • More classes
  • A simple classification problem
    • +
  • Cancer Data again now with Decision Trees and other Methods
    • +
  • Other measures in classification studies: Cancer Data again
    • @@ -176,6 +186,8 @@ and the model is specified in term of \( K-1 \) so-called log-odds or
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    • diff --git a/doc/pub/week38/html/._week38-bs017.html b/doc/pub/week38/html/._week38-bs017.html index c025008c3..36787b4f4 100644 --- a/doc/pub/week38/html/._week38-bs017.html +++ b/doc/pub/week38/html/._week38-bs017.html @@ -62,7 +62,15 @@ Automatically generated HTML file from DocOnce source ('Extending to more predictors', 2, None, '___sec14'), ('Including more classes', 2, None, '___sec15'), ('More classes', 2, None, '___sec16'), - ('A simple classification problem', 2, None, '___sec17')]} + ('A simple classification problem', 2, None, '___sec17'), + ('Cancer Data again now with Decision Trees and other Methods', + 2, + None, + '___sec18'), + ('Other measures in classification studies: Cancer Data again', + 2, + None, + '___sec19')]} end of tocinfo --> @@ -118,6 +126,8 @@ MathJax.Hub.Config({
  • Including more classes
  • More classes
  • A simple classification problem
    • +
  • Cancer Data again now with Decision Trees and other Methods
    • +
  • Other measures in classification studies: Cancer Data again
    • @@ -187,6 +197,8 @@ methods.
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    • diff --git a/doc/pub/week38/html/._week38-bs018.html b/doc/pub/week38/html/._week38-bs018.html index 955b2c7e7..3cf117d6f 100644 --- a/doc/pub/week38/html/._week38-bs018.html +++ b/doc/pub/week38/html/._week38-bs018.html @@ -62,7 +62,15 @@ Automatically generated HTML file from DocOnce source ('Extending to more predictors', 2, None, '___sec14'), ('Including more classes', 2, None, '___sec15'), ('More classes', 2, None, '___sec16'), - ('A simple classification problem', 2, None, '___sec17')]} + ('A simple classification problem', 2, None, '___sec17'), + ('Cancer Data again now with Decision Trees and other Methods', + 2, + None, + '___sec18'), + ('Other measures in classification studies: Cancer Data again', + 2, + None, + '___sec19')]} end of tocinfo --> @@ -118,6 +126,8 @@ MathJax.Hub.Config({
  • Including more classes
  • More classes
  • A simple classification problem
    • +
  • Cancer Data again now with Decision Trees and other Methods
    • +
  • Other measures in classification studies: Cancer Data again
    • @@ -183,7 +193,6 @@ MathJax.Hub.Config({ main()

    -

    diff --git a/doc/pub/week38/html/._week38-bs019.html b/doc/pub/week38/html/._week38-bs019.html new file mode 100644 index 000000000..20f4767f5 --- /dev/null +++ b/doc/pub/week38/html/._week38-bs019.html @@ -0,0 +1,217 @@ + + + + + + + + +Data Analysis and Machine Learning: Logistic Regression + + + + + + + + + + + + + + + + + + + + + + + +
    +
    + + Data Analysis and Machine Learning: Logistic Regression +
    + +
    + +
    +
    + + +
    + +

     

     

     

    + + + + +

    Cancer Data again now with Decision Trees and other Methods

    +

    + + +

    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.linear_model import LogisticRegression
+
+# 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)
+# Logistic Regression
+logreg = LogisticRegression(solver='lbfgs')
+logreg.fit(X_train, y_train)
+print("Test set accuracy with Logistic Regression: {:.2f}".format(logreg.score(X_test,y_test)))
+#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)
+# Logistic Regression
+logreg.fit(X_train_scaled, y_train)
+print("Test set accuracy Logistic Regression with scaled data: {:.2f}".format(logreg.score(X_test_scaled,y_test)))
+
    +

    +

    + +

    + + +
    + + + + + + + +
    + +
    + + + + + + diff --git a/doc/pub/week38/html/._week38-bs020.html b/doc/pub/week38/html/._week38-bs020.html new file mode 100644 index 000000000..5df1745ca --- /dev/null +++ b/doc/pub/week38/html/._week38-bs020.html @@ -0,0 +1,235 @@ + + + + + + + + +Data Analysis and Machine Learning: Logistic Regression + + + + + + + + + + + + + + + + + + + + + + + +
    +
    + + Data Analysis and Machine Learning: Logistic Regression +
    + +
    + +
    +
    + + +
    + +

     

     

     

    + + + + +

    Other measures in classification studies: Cancer Data again

    +

    + + +

    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.linear_model import LogisticRegression
+
+# 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)
+# Logistic Regression
+logreg = LogisticRegression(solver='lbfgs')
+logreg.fit(X_train, y_train)
+print("Test set accuracy with Logistic Regression: {:.2f}".format(logreg.score(X_test,y_test)))
+#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)
+# Logistic Regression
+logreg.fit(X_train_scaled, y_train)
+print("Test set accuracy Logistic Regression with scaled data: {:.2f}".format(logreg.score(X_test_scaled,y_test)))
+
+
+from sklearn.preprocessing import LabelEncoder
+from sklearn.model_selection import cross_validate
+#Cross validation
+accuracy = cross_validate(logreg,X_test_scaled,y_test,cv=10)['test_score']
+print(accuracy)
+print("Test set accuracy with Logistic Regression  and scaled data: {:.2f}".format(logreg.score(X_test_scaled,y_test)))
+
+
+import scikitplot as skplt
+y_pred = logreg.predict(X_test_scaled)
+skplt.metrics.plot_confusion_matrix(y_test, y_pred, normalize=True)
+plt.show()
+y_probas = logreg.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()
+
    +

    + +

    + +

    + + +
    + + + + + + + +
    + +
    + + + + + + diff --git a/doc/pub/week38/html/week38-bs.html b/doc/pub/week38/html/week38-bs.html index 6d00cb4a2..5a7692ec7 100644 --- a/doc/pub/week38/html/week38-bs.html +++ b/doc/pub/week38/html/week38-bs.html @@ -62,7 +62,15 @@ Automatically generated HTML file from DocOnce source ('Extending to more predictors', 2, None, '___sec14'), ('Including more classes', 2, None, '___sec15'), ('More classes', 2, None, '___sec16'), - ('A simple classification problem', 2, None, '___sec17')]} + ('A simple classification problem', 2, None, '___sec17'), + ('Cancer Data again now with Decision Trees and other Methods', + 2, + None, + '___sec18'), + ('Other measures in classification studies: Cancer Data again', + 2, + None, + '___sec19')]} end of tocinfo --> @@ -118,6 +126,8 @@ MathJax.Hub.Config({
  • Including more classes
  • More classes
  • A simple classification problem
    • +
  • Cancer Data again now with Decision Trees and other Methods
    • +
  • Other measures in classification studies: Cancer Data again
    • @@ -176,7 +186,7 @@ MathJax.Hub.Config({
  • 9
  • 10
  • ...
    • -
  • 19
    • +
  • 21
  • »
    • diff --git a/doc/pub/week38/html/week38-reveal.html b/doc/pub/week38/html/week38-reveal.html index cc3db2e84..04a81430a 100644 --- a/doc/pub/week38/html/week38-reveal.html +++ b/doc/pub/week38/html/week38-reveal.html @@ -682,6 +682,93 @@ methods. +
    +

    Cancer Data again now with Decision Trees and other Methods

    +

    + + +

    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.linear_model import LogisticRegression
+
+# 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)
+# Logistic Regression
+logreg = LogisticRegression(solver='lbfgs')
+logreg.fit(X_train, y_train)
+print("Test set accuracy with Logistic Regression: {:.2f}".format(logreg.score(X_test,y_test)))
+#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)
+# Logistic Regression
+logreg.fit(X_train_scaled, y_train)
+print("Test set accuracy Logistic Regression with scaled data: {:.2f}".format(logreg.score(X_test_scaled,y_test)))
+
    +
    + + +
    +

    Other measures in classification studies: Cancer Data again

    +

    + + +

    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.linear_model import LogisticRegression
+
+# 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)
+# Logistic Regression
+logreg = LogisticRegression(solver='lbfgs')
+logreg.fit(X_train, y_train)
+print("Test set accuracy with Logistic Regression: {:.2f}".format(logreg.score(X_test,y_test)))
+#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)
+# Logistic Regression
+logreg.fit(X_train_scaled, y_train)
+print("Test set accuracy Logistic Regression with scaled data: {:.2f}".format(logreg.score(X_test_scaled,y_test)))
+
+
+from sklearn.preprocessing import LabelEncoder
+from sklearn.model_selection import cross_validate
+#Cross validation
+accuracy = cross_validate(logreg,X_test_scaled,y_test,cv=10)['test_score']
+print(accuracy)
+print("Test set accuracy with Logistic Regression  and scaled data: {:.2f}".format(logreg.score(X_test_scaled,y_test)))
+
+
+import scikitplot as skplt
+y_pred = logreg.predict(X_test_scaled)
+skplt.metrics.plot_confusion_matrix(y_test, y_pred, normalize=True)
+plt.show()
+y_probas = logreg.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()
+
    +
    + + diff --git a/doc/pub/week38/html/week38-solarized.html b/doc/pub/week38/html/week38-solarized.html index b0cebc0bb..a21c3811f 100644 --- a/doc/pub/week38/html/week38-solarized.html +++ b/doc/pub/week38/html/week38-solarized.html @@ -56,7 +56,15 @@ div { text-align: justify; text-justify: inter-word; } ('Extending to more predictors', 2, None, '___sec14'), ('Including more classes', 2, None, '___sec15'), ('More classes', 2, None, '___sec16'), - ('A simple classification problem', 2, None, '___sec17')]} + ('A simple classification problem', 2, None, '___sec17'), + ('Cancer Data again now with Decision Trees and other Methods', + 2, + None, + '___sec18'), + ('Other measures in classification studies: Cancer Data again', + 2, + None, + '___sec19')]} end of tocinfo --> @@ -582,6 +590,91 @@ methods. main()

    +









    + +

    Cancer Data again now with Decision Trees and other Methods

    +

    + + +

    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.linear_model import LogisticRegression
+
+# 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)
+# Logistic Regression
+logreg = LogisticRegression(solver='lbfgs')
+logreg.fit(X_train, y_train)
+print("Test set accuracy with Logistic Regression: {:.2f}".format(logreg.score(X_test,y_test)))
+#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)
+# Logistic Regression
+logreg.fit(X_train_scaled, y_train)
+print("Test set accuracy Logistic Regression with scaled data: {:.2f}".format(logreg.score(X_test_scaled,y_test)))
+
    +

    +









    + +

    Other measures in classification studies: Cancer Data again

    +

    + + +

    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.linear_model import LogisticRegression
+
+# 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)
+# Logistic Regression
+logreg = LogisticRegression(solver='lbfgs')
+logreg.fit(X_train, y_train)
+print("Test set accuracy with Logistic Regression: {:.2f}".format(logreg.score(X_test,y_test)))
+#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)
+# Logistic Regression
+logreg.fit(X_train_scaled, y_train)
+print("Test set accuracy Logistic Regression with scaled data: {:.2f}".format(logreg.score(X_test_scaled,y_test)))
+
+
+from sklearn.preprocessing import LabelEncoder
+from sklearn.model_selection import cross_validate
+#Cross validation
+accuracy = cross_validate(logreg,X_test_scaled,y_test,cv=10)['test_score']
+print(accuracy)
+print("Test set accuracy with Logistic Regression  and scaled data: {:.2f}".format(logreg.score(X_test_scaled,y_test)))
+
+
+import scikitplot as skplt
+y_pred = logreg.predict(X_test_scaled)
+skplt.metrics.plot_confusion_matrix(y_test, y_pred, normalize=True)
+plt.show()
+y_probas = logreg.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()
+
    +

    diff --git a/doc/pub/week38/html/week38.html b/doc/pub/week38/html/week38.html index faa233a10..0ae1d126a 100644 --- a/doc/pub/week38/html/week38.html +++ b/doc/pub/week38/html/week38.html @@ -61,7 +61,15 @@ div { text-align: justify; text-justify: inter-word; } ('Extending to more predictors', 2, None, '___sec14'), ('Including more classes', 2, None, '___sec15'), ('More classes', 2, None, '___sec16'), - ('A simple classification problem', 2, None, '___sec17')]} + ('A simple classification problem', 2, None, '___sec17'), + ('Cancer Data again now with Decision Trees and other Methods', + 2, + None, + '___sec18'), + ('Other measures in classification studies: Cancer Data again', + 2, + None, + '___sec19')]} end of tocinfo --> @@ -587,6 +595,91 @@ methods. main()

    +









    + +

    Cancer Data again now with Decision Trees and other Methods

    +

    + + +

    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.linear_model import LogisticRegression
+
+# 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)
+# Logistic Regression
+logreg = LogisticRegression(solver='lbfgs')
+logreg.fit(X_train, y_train)
+print("Test set accuracy with Logistic Regression: {:.2f}".format(logreg.score(X_test,y_test)))
+#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)
+# Logistic Regression
+logreg.fit(X_train_scaled, y_train)
+print("Test set accuracy Logistic Regression with scaled data: {:.2f}".format(logreg.score(X_test_scaled,y_test)))
+
    +

    +









    + +

    Other measures in classification studies: Cancer Data again

    +

    + + +

    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.linear_model import LogisticRegression
+
+# 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)
+# Logistic Regression
+logreg = LogisticRegression(solver='lbfgs')
+logreg.fit(X_train, y_train)
+print("Test set accuracy with Logistic Regression: {:.2f}".format(logreg.score(X_test,y_test)))
+#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)
+# Logistic Regression
+logreg.fit(X_train_scaled, y_train)
+print("Test set accuracy Logistic Regression with scaled data: {:.2f}".format(logreg.score(X_test_scaled,y_test)))
+
+
+from sklearn.preprocessing import LabelEncoder
+from sklearn.model_selection import cross_validate
+#Cross validation
+accuracy = cross_validate(logreg,X_test_scaled,y_test,cv=10)['test_score']
+print(accuracy)
+print("Test set accuracy with Logistic Regression  and scaled data: {:.2f}".format(logreg.score(X_test_scaled,y_test)))
+
+
+import scikitplot as skplt
+y_pred = logreg.predict(X_test_scaled)
+skplt.metrics.plot_confusion_matrix(y_test, y_pred, normalize=True)
+plt.show()
+y_probas = logreg.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()
+
    +

    diff --git a/doc/pub/week38/ipynb/ipynb-week38-src.tar.gz b/doc/pub/week38/ipynb/ipynb-week38-src.tar.gz index b704baad8..8f0c907b2 100644 Binary files a/doc/pub/week38/ipynb/ipynb-week38-src.tar.gz and b/doc/pub/week38/ipynb/ipynb-week38-src.tar.gz differ diff --git a/doc/pub/week38/ipynb/week38.ipynb b/doc/pub/week38/ipynb/week38.ipynb index 624f17304..a45db93d6 100644 --- a/doc/pub/week38/ipynb/week38.ipynb +++ b/doc/pub/week38/ipynb/week38.ipynb @@ -675,6 +675,109 @@ "if __name__ == \"__main__\":\n", " main()" ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Cancer Data again now with Decision Trees and other Methods" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "from sklearn.model_selection import train_test_split \n", + "from sklearn.datasets import load_breast_cancer\n", + "from sklearn.linear_model import LogisticRegression\n", + "\n", + "# Load the data\n", + "cancer = load_breast_cancer()\n", + "\n", + "X_train, X_test, y_train, y_test = train_test_split(cancer.data,cancer.target,random_state=0)\n", + "print(X_train.shape)\n", + "print(X_test.shape)\n", + "# Logistic Regression\n", + "logreg = LogisticRegression(solver='lbfgs')\n", + "logreg.fit(X_train, y_train)\n", + "print(\"Test set accuracy with Logistic Regression: {:.2f}\".format(logreg.score(X_test,y_test)))\n", + "#now scale the data\n", + "from sklearn.preprocessing import StandardScaler\n", + "scaler = StandardScaler()\n", + "scaler.fit(X_train)\n", + "X_train_scaled = scaler.transform(X_train)\n", + "X_test_scaled = scaler.transform(X_test)\n", + "# Logistic Regression\n", + "logreg.fit(X_train_scaled, y_train)\n", + "print(\"Test set accuracy Logistic Regression with scaled data: {:.2f}\".format(logreg.score(X_test_scaled,y_test)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Other measures in classification studies: Cancer Data again" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "from sklearn.model_selection import train_test_split \n", + "from sklearn.datasets import load_breast_cancer\n", + "from sklearn.linear_model import LogisticRegression\n", + "\n", + "# Load the data\n", + "cancer = load_breast_cancer()\n", + "\n", + "X_train, X_test, y_train, y_test = train_test_split(cancer.data,cancer.target,random_state=0)\n", + "print(X_train.shape)\n", + "print(X_test.shape)\n", + "# Logistic Regression\n", + "logreg = LogisticRegression(solver='lbfgs')\n", + "logreg.fit(X_train, y_train)\n", + "print(\"Test set accuracy with Logistic Regression: {:.2f}\".format(logreg.score(X_test,y_test)))\n", + "#now scale the data\n", + "from sklearn.preprocessing import StandardScaler\n", + "scaler = StandardScaler()\n", + "scaler.fit(X_train)\n", + "X_train_scaled = scaler.transform(X_train)\n", + "X_test_scaled = scaler.transform(X_test)\n", + "# Logistic Regression\n", + "logreg.fit(X_train_scaled, y_train)\n", + "print(\"Test set accuracy Logistic Regression with scaled data: {:.2f}\".format(logreg.score(X_test_scaled,y_test)))\n", + "\n", + "\n", + "from sklearn.preprocessing import LabelEncoder\n", + "from sklearn.model_selection import cross_validate\n", + "#Cross validation\n", + "accuracy = cross_validate(logreg,X_test_scaled,y_test,cv=10)['test_score']\n", + "print(accuracy)\n", + "print(\"Test set accuracy with Logistic Regression and scaled data: {:.2f}\".format(logreg.score(X_test_scaled,y_test)))\n", + "\n", + "\n", + "import scikitplot as skplt\n", + "y_pred = logreg.predict(X_test_scaled)\n", + "skplt.metrics.plot_confusion_matrix(y_test, y_pred, normalize=True)\n", + "plt.show()\n", + "y_probas = logreg.predict_proba(X_test_scaled)\n", + "skplt.metrics.plot_roc(y_test, y_probas)\n", + "plt.show()\n", + "skplt.metrics.plot_cumulative_gain(y_test, y_probas)\n", + "plt.show()" + ] } ], "metadata": {},