Chapter 3: A Tour of Machine Learning Classifiers Using scikit-learn

Chapter Outline

• Choosing a classification algorithm

• First steps with scikit-learn -- training a perceptron

• Modeling class probabilities via logistic regression

  • Logistic regression intuition and conditional probabilities

  • Learning the weights of the logistic cost function

  • Converting an Adaline implementation into an algorithm for logistic regression

  • Training a logistic regression model with scikit-learn

  • Tackling overfitting via regularization

• Maximum margin classification with support vector machines

  • Maximum margin intuition

  • Dealing with a nonlinearly separable case using slack variables

  • Alternative implementations in scikit-learn

• Solving nonlinear problems using a kernel SVM

  • Kernel methods for linearly inseparable data

  • Using the kernel trick to find separating hyperplanes in high-dimensional space

• Decision tree learning

  • Maximizing information gain – getting the most bang for your buck

  • Building a decision tree

  • Combining multiple decision trees via random forests

• K-nearest neighbors – a lazy learning algorithm

• Summary

Please refer to the README.md file in ../ch01 for more information about running the code examples.