What is a decision tree mainly used for in machine learning?Review LaterData CleaningFeature SelectionData ClassificationData Aggregation

Main Use of Decision Trees in Machine Learning

Decision trees are predominantly used for Data Classification in machine learning. This method involves splitting the dataset into branches based on feature values, creating a tree structure. Each internal node of the tree represents a decision based on an attribute, each branch represents the outcome of that decision, and each leaf node represents a class label.

Explanation of Options:

1. Data Cleaning: This process involves removing or correcting errors in the dataset. While important, it is not the primary function of decision trees.
2. Feature Selection: This is the process of selecting a subset of relevant features for model construction. Decision trees can help identify important features, but this is not their main use.
3. Data Classification: Correct answer. Decision trees classify data into distinct categories based on feature values.
4. Data Aggregation: This involves compiling data from different sources. Decision trees do not perform aggregation.

Final Answer

Data Classification is the primary use of decision trees in machine learning.