What are decision trees in machine learning?

What are decision trees in machine learning?

Decision trees are a type of supervised learning algorithm used for classification and regression tasks. They model decisions and their possible consequences as a tree-like structure.

How do decision trees work?

Decision trees work by splitting the data into subsets based on the value of input features. Each split is made to maximize the separation of the target variable.

What are the advantages of using decision trees?

Some advantages include their simplicity, interpretability, and ability to handle both numerical and categorical data.

Are there any disadvantages?

Yes, decision trees can be prone to overfitting, especially with complex trees. They can also be sensitive to noisy data.

Can decision trees be used for regression?

Yes, decision trees can be used for regression tasks, where they predict continuous values instead of categories.