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Chapter 7: Combining Different Models for Ensemble Learning

Chapter Outline

  • Learning with ensembles
  • Combining classifiers via majority vote
    • Implementing a simple majority vote classifier
    • Using the majority voting principle to make predictions
    • Evaluating and tuning the ensemble classifier
  • Bagging – building an ensemble of classifiers from bootstrap samples
    • Bagging in a nutshell
    • Applying bagging to classify samples in the Wine dataset
  • Leveraging weak learners via adaptive boosting
    • How boosting works
    • Applying AdaBoost using scikit-learn
  • Gradient boosting -- training an ensemble based on loss gradients
    • Comparing AdaBoost with gradient boosting
    • Outlining the general gradient boosting algorithm
    • Explaining the gradient boosting algorithm for classification
    • Illustrating gradient boosting for classification
    • Using XGBoost
  • Summary

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