chore: import upstream snapshot with attribution

code/ch03/README.md

@@ -0,0 +1,27 @@
+Sebastian Raschka, 2015
+
+Python Machine Learning - Code Examples
+
+
+## Chapter 3 - A Tour of Machine Learning Classifiers Using Scikit-learn
+
+- Choosing a classification algorithm
+- First steps with scikit-learn
+    - Training a perceptron via scikit-learn
+- Modeling class probabilities via logistic regression
+    - Logistic regression intuition and conditional probabilities
+    - Learning the weights of the logistic cost function
+    - Training a logistic regression model with scikit-learn
+    - Tackling overfitting via regularization
+- Maximum margin classification with support vector machines
+    - Maximum margin intuition
+    - Dealing with the nonlinearly separable case using slack variables
+    - Alternative implementations in scikit-learn
+- Solving nonlinear problems using a kernel SVM
+    - Using the kernel trick to find separating hyperplanes in higher dimensional space
+- Decision tree learning
+    - Maximizing information gain – getting the most bang for the buck
+    - Building a decision tree
+    - Combining weak to strong learners via random forests
+- K-nearest neighbors – a lazy learning algorithm
+- Summary

code/ch03/tree.dot

@@ -0,0 +1,20 @@
+digraph Tree {
+node [shape=box] ;
+0 [label="petal width <= 0.75\nentropy = 1.5799\nsamples = 105\nvalue = [34, 32, 39]"] ;
+1 [label="entropy = 0.0\nsamples = 34\nvalue = [34, 0, 0]"] ;
+0 -> 1 [labeldistance=2.5, labelangle=45, headlabel="True"] ;
+2 [label="petal length <= 4.95\nentropy = 0.993\nsamples = 71\nvalue = [0, 32, 39]"] ;
+0 -> 2 [labeldistance=2.5, labelangle=-45, headlabel="False"] ;
+3 [label="petal width <= 1.65\nentropy = 0.4306\nsamples = 34\nvalue = [0, 31, 3]"] ;
+2 -> 3 ;
+4 [label="entropy = 0.0\nsamples = 30\nvalue = [0, 30, 0]"] ;
+3 -> 4 ;
+5 [label="entropy = 0.8113\nsamples = 4\nvalue = [0, 1, 3]"] ;
+3 -> 5 ;
+6 [label="petal length <= 5.05\nentropy = 0.1793\nsamples = 37\nvalue = [0, 1, 36]"] ;
+2 -> 6 ;
+7 [label="entropy = 0.8113\nsamples = 4\nvalue = [0, 1, 3]"] ;
+6 -> 7 ;
+8 [label="entropy = 0.0\nsamples = 33\nvalue = [0, 0, 33]"] ;
+6 -> 8 ;
+}