Decision Trees
Decision trees are a non-parametric supervised learning method used for classification and regression tasks. They work by creating a tree-like model of decisions and their possible consequences, where each internal node represents a 'test' on an attribute, each branch represents the outcome of the test, and each leaf node represents a class label or a numerical value. These trees are intuitive and easily interpretable, breaking down complex decisions into a series of simpler ones.
A decision tree is like a flowchart that helps you make a choice by asking a series of yes or no questions.
Decision trees provide a simple, visible, and interpretable way to model decisions, making them valuable for understanding the factors influencing an outcome.
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