Decision Tree

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A decision tree is a simple diagram used to make decisions or predictions. It has three main types of nodes:

1. Root Node: The starting point of the tree.
2. Internal Nodes: These are decision points that split data based on certain conditions.
3. Leaf Nodes: The endpoints that show the final result or category.

Example of Decision Tree for Mammal Classification

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Here is an example of a decision tree used to classify whether an animal is a mammal or not:

Functions in Decision Tree Induction

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Four functions are used in this algorithm:

1. CreateNode(): Makes a new point in the tree.
2. Find_best_split(): Finds the best way to divide data.
3. Classify(): Assigns a category to a data point.
4. Stopping_cond(): Decides when to stop splitting.