Python Data Analysis Cookbook

Chapter 9. Ensemble Learning and Dimensionality Reduction

In this chapter, we will cover the following recipes:

• Recursively eliminating features
• Applying principal component analysis for dimensionality reduction
• Applying linear discriminant analysis for dimensionality reduction
• Stacking and majority voting for multiple models
• Learning with random forests
• Fitting noisy data with the RANSAC algorithm
• Bagging to improve results
• Boosting for better learning
• Nesting cross-validation
• Reusing models with joblib
• Hierarchically clustering data
• Taking a Theano tour

Introduction

In the 1983 War Games movie, a computer made life and death decisions that could have resulted in World War III. As far as I know, technology wasn't able to pull off such feats at the time. However, ...