How do decision trees work in AI and what are their limitations?

Decision Trees are supervised learning models that split data into subsets based on feature values, creating a tree-like structure. Each node in the tree represents a decision based on a feature, and leaves represent outcomes or classifications. They are simple and interpretable, making them popular for classification and regression tasks. Many AI Bootcamp Online courses cover Decision Trees as part of their curriculum due to their fundamental role in machine learning.

However, decision trees are prone to overfitting, especially with complex datasets, and can be unstable with small variations in data. To overcome these issues, ensemble methods like Random Forest or Gradient Boosting are often used.