What is a Decision Tree?
A decision tree is the most powerful and popular tool for classification and prediction.
A Decision tree is a flowchart-like tree structure where:
- each internal node denotes a test on an attribute/feature,
- each branch represents an outcome of the test, and
- each leaf node (terminal node) holds a class label (Yes and No in this case).
Decision Trees for Regression - Regression Trees
Regression tree analysis is used when the predicted outcome can be considered a real number (e.g., the price of a house or a patient's length of stay in a hospital).
You might encounter the term 'CART' while building ML models. It's nothing new but the same old Decision Tree since it can be used for both Classification and Regression.
CART = Classification and Regression Trees, an umbrella term for:
Classification Trees: where the target variable is categorical, the tree is used to identify the "class" within which a target variable would likely fall into.
Regression Trees: where the target variable is continuous, and the tree is used to predict its value.
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