This lesson first reviews the topics learned in previous lesson about training a decision tress using sklearn, how to handle the model not generalizing due to overfitting of the data.
In this lesson, we learn about how to best split a decision tree and different classification criteria that can be used to split a tree. We dive deep using an example and splitting the tree using misclassification criteria. Additionally, different stopping criteria to break the iterative tree split criteria are discussed.
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- structure of a decision tree: nodes & leaves
- depth of a decision tree & levels
- rules & conditions, thresholds
- misclassification rate
- impurity criteria (i.e. MSE)
- decision trees can be used to solve regression problems
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