A form of supervised machine learning which attempts to build a tree like model of decisions to predict outcomes.
Training
- Start with all training data at the root of the tree
- Find best features and threshold to split the data
- Split the data into subsets based on the decision boundary
- Recursively repeat this for each subset
- Stop once you reach some sort of stopping criterion