Components of a Decision Tree
Before we dive into the types of Decision Tree Algorithms, we need to know about the following important terms:
- Root Node: It is the topmost node in the tree, which represents the complete dataset. It is the starting point of the decision-making process.
- Internal Node: A node that symbolizes a choice regarding an input feature. Branching off of internal nodes connects them to leaf nodes or other internal nodes.
- Leaf/Terminal Node: A node without any child nodes that indicates a class label or a numerical value.
- Parent Node: The node that divides into one or more child nodes.
- Child Node: The nodes that emerge when a parent node is split.
Decision Tree Algorithms
Decision trees are a type of machine-learning algorithm that can be used for both classification and regression tasks. They work by learning simple decision rules inferred from the data features. These rules can then be used to predict the value of the target variable for new data samples.
Decision trees are represented as tree structures, where each internal node represents a feature, each branch represents a decision rule, and each leaf node represents a prediction. The algorithm works by recursively splitting the data into smaller and smaller subsets based on the feature values. At each node, the algorithm chooses the feature that best splits the data into groups with different target values.
Table of Content
- Understanding Decision Trees
- Components of a Decision Tree
- Working of the Decision Tree Algorithm
- Understanding the Key Mathematical Concepts Behind Decision Trees
- Types of Decision Tree Algorithms
- ID3 (Iterative Dichotomiser 3)
- C4.5
- CART (Classification and Regression Trees)
- CHAID (Chi-Square Automatic Interaction Detection)
- MARS (Multivariate Adaptive Regression Splines)
- Implementation of Decision Tree Algorithms
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