How do decision trees work?

Can you explain how decision trees work in machine learning?

Sure! Decision trees are a type of model used for classification and regression tasks. They split data into branches based on feature values.

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 to decision trees?

Yes, they can be prone to overfitting, especially with complex trees. Pruning techniques are often used to mitigate this.

How do you prevent overfitting in decision trees?

You can prevent overfitting by using techniques like pruning, setting a maximum depth, or requiring a minimum number of samples per leaf.