What are decision trees and how do I interpret them?

What are decision trees and how do I interpret them?

Decision trees are a type of model used for classification and regression. They split data into branches to make decisions.

How do I read a decision tree?

To read a decision tree, start from the root node and follow the branches based on the feature values until you reach a leaf node.

What do the leaf nodes represent?

Leaf nodes represent the final output or decision of the model, indicating the predicted class or value.

Can decision trees handle missing values?

Yes, decision trees can handle missing values by using surrogate splits or by ignoring the missing data during the split.

What are the advantages of using decision trees?

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