Decision tree

A decision tree is a schematic, tree-shaped diagram used to determine a course of action or show a statistical probability. Each branch of the decision tree represents a possible decision, occurrence or reaction. The tree is structured to show how and why one choice may lead to the next, with the use of the branches indicating each option is mutually exclusive.

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Decision Trees are an important type of algorithm for predictive modeling machine learning.
The representation of the decision tree model is a binary tree. This is your binary tree from algorithms and data structures, nothing too fancy. Each node represents a single input variable (x) and a split point on that variable (assuming the variable is numeric).
A decision tree is faster as compared to linear algorithms because it is generally faster. Trees work with nodes and can be fast as compared to other linear algorithms.

The leaf nodes of the tree contain an output variable (y) which is used to make a prediction.  Predictions are made by walking the splits of the tree until arriving at a leaf node and output the class value at that leaf node.
Trees are fast to learn and very fast for making predictions. They are also often accurate for a broad range of problems and do not require any special preparation for your data.

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