Answer: c. To classify data by splitting it into branches based on feature values.
Explanation:
Decision trees are used for both classification and regression tasks. For classification, they split the data into branches based on the values of the features, leading to a decision at each branch.
This process continues until the data is categorized into distinct classes or a stopping criterion is met.
The primary goal of a decision tree in machine learning is c. To classify data by splitting it into branches based on feature values.
Decision trees work by recursively partitioning the data into subsets based on the values of input features, creating a tree-like model of decisions. Each internal node represents a feature or attribute, each branch represents a decision rule, and each leaf node represents an outcome or class label.
This hierarchical structure allows decision trees to handle both categorical and numerical data, making them versatile for various classification tasks. Decision trees are intuitive and easy to interpret, as they visually and logically represent the decision-making process. While they can also be used for regression (predicting continuous values), their primary use is classification.