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, in 1997, the Deep Blue supercomputer did manage to beat a world chess champion. In 2005, a Stanford self-driving car drove by itself for more than 130 kilometers in a desert. In 2007, the car of another team drove through regular...
