Machine-Learning-in-Action
python codes with numpy -- Machine Learning in Action
2.1 The k-Nearest Neighbors classification algorithm
2.2 using kNN on results from a dating site
2.3 using kNN on a handwriting recognition system
3.2 Plotting trees in Python with Matplotlib annotations
3.3 Testing and storing the classifier
3.4 Example: using decision trees to predict contact lens type
4.1 Classifying with Bayesian decision theory
4.2 Conditional probability
4.3 Classifying with conditional probabilities
4.4 Document classification with naïve Bayes
4.5 Classifying text with Python
4.6 Example: classifying spam email with naïve Bayes
Chapter 5: Logistic regression
5.1 Classification with logistic regression and the sigmoid function: a tractable step function
5.2 Using optimization to find the best regression coefficients
5.3 Example: estimating horse fatalities from colic
Chapter 6: Support vector machines
6.1 Separating data with the maximum margin
6.2 Finding the maximum margin
6.3 Efficient optimization with the SMO algorithm
Chapter 7 Improving classification with the AdaBoost meta-algorithm
7.1 Classifiers using multiple samples of the dataset
7.2 Train: improving the classifier by focusing on errors
7.3 Creating a weak learner with a decision stump
7.4 Implementing the full AdaBoost algorithm
7.5 Test: classifying with AdaBoost
7.6 Example: AdaBoost on a difficult dataset
7.7 Classification imbalance