What are decision trees in AI?

What are decision trees in AI?

Decision trees are a type of model used in AI for decision-making and classification tasks. They represent decisions and their possible consequences as a tree-like structure.

How do they work?

They work by splitting the data into subsets based on the value of input features, creating branches for each possible outcome until a decision is made.

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 me an example of a decision tree?

Sure! For instance, a decision tree for deciding whether to play outside might consider factors like weather, temperature, and time of day.