Working of the Decision Tree Algorithm
Whether employed for regression or classification, a decision tree method provides a flexible and easily interpreted machine learning technique. To create choices depending on the input features, it constructs a structure like a tree. Leaf nodes in the tree indicate the ultimate results, whereas nodes in the tree represent decisions or tests on the feature values.
Here’s a detailed breakdown of how the decision tree algorithm works:
- With all the data at its starting point, the process is the root node. In order to effectively divide the data into discrete classes or values, the algorithm chooses a feature together with a threshold. Depending on the job (classification or regression), the feature and threshold are selected to maximize information gain or decrease impurity.
- Depending on the outcome of the feature test, the data is separated into subgroups. When a characteristic like “Age” is used with a threshold of 30, for instance, the data is divided into two subsets: records with Age less than or equal to 30, and records with Age more than 30.
- For every subgroup, the splitting procedure is repeated, resulting in child nodes. Up until a halting condition is satisfied, this recursive process keeps going. A minimal amount of data points in a node, a predetermined tree depth, or the lack of additional information gained from splits beyond that point are examples of common stopping criteria.
- A node turns into a leaf node when a stopping requirement is satisfied. The final judgment or forecast is represented by the leaf nodes. Each leaf node is classified using the class label that is most common inside the subset. In a regression, the target variable’s mean or median value within the subset is usually found in the leaf node.
- The tree structure that is produced can be understood. The reasoning of the model can be intuitively understood by viewing a decision path from the root to a leaf node as a set of rules.
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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