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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.