What are decision trees and how can I interpret them?

What are decision trees and how can I interpret them?

Decision trees are a type of model used for classification and regression tasks. They split data into branches based on feature values, making decisions at each node. To interpret them, you can follow the path from the root to the leaves, which shows how decisions are made based on input features.

Can you explain how to visualize a decision tree?

Certainly! You can visualize a decision tree using libraries like Matplotlib or Graphviz in Python. These tools allow you to create a graphical representation of the tree structure, showing the splits and decisions clearly.

What are the advantages of using decision trees?

Decision trees are easy to interpret and visualize, handle both numerical and categorical data, and require little data preprocessing. They also perform well on large datasets.

Are there any limitations to decision trees?

Yes, decision trees can be prone to overfitting, especially with complex trees. They can also be sensitive to small changes in the data, which may lead to different tree structures.