What are decision trees in machine learning?

What are decision trees in machine learning?

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

How do they work in making predictions?

They work by creating a tree-like structure where each node represents a decision based on a feature, leading to a final prediction at the leaves.

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, they can be prone to overfitting, especially with complex trees, and they may not perform well on imbalanced datasets.

Can you give an example of a decision tree?

Sure! For instance, a decision tree for deciding whether to play outside might consider weather conditions, temperature, and wind.