Definition: A decision tree is a machine learning algorithm that uses a tree-like structure to make decisions.

Detailed Description:

Decision trees consist of nodes and branches. Internal nodes represent features, while leaf nodes represent class labels. The algorithm recursively splits the data into subsets based on feature values until a decision is made.

Decision trees are used in various applications, including:

• Classification: Predicting the category or class of a data point.
• Regression: Predicting a continuous numerical value.
• Data mining: Discovering patterns and relationships in data.

Related Terms:

• Machine Learning
• Supervised Learning
• Classification
• Regression
• Entropy
• Information Gain

FAQs:

1. How do decision trees work?

   • Decision trees make decisions by following a series of if-else conditions based on feature values.

2. What are the advantages of using decision trees?

   • Decision trees are easy to understand and interpret, and they can handle both numerical and categorical data.

3. What are the challenges of using decision trees?

   • Decision trees can be prone to overfitting, especially with large datasets.