Tree Algorithms and Ensembles

In this chapter we will cover the following recipes:

• Doing basic classifications with decision trees
• Visualizing a decision tree with pydot
• Tuning a decision tree
• Using decision trees for regression
• Reducing overfitting with cross-validation
• Implementing random forest regression
• Bagging regression with nearest neighbor
• Tuning gradient boosting trees
• Tuning an AdaBoost regressor
• Writing a stacking aggregator with scikit-learn