Data Science Bookcamp

22 Training nonlinear classifiers with decision tree techniques

This section covers

• Classifying datasets that are not linearly separable
• Automatically generating if/else logical rules from training data
• What is a decision tree?
• What is a random forest?
• Training tree-based models using scikit-learn

Thus far, we have investigated supervised learning techniques that rely on the geometry of data. This association between learning and geometry does not align with our everyday experiences. On a cognitive level, people do not learn through abstract spatial analysis; they learn by making logical inferences about the world. These inferences can then be shared with others. A toddler realizes that by throwing a fake tantrum, they can sometimes get an ...