What is the difference between Decision Tree and Random Forest?

Decision Tree:-- A Decision Tree is a supervised machine learning algorithm used for both classification and regression tasks. It represents decisions in a tree-like structure where internal nodes denote features, branches represent decision rules, and leaf nodes give the final prediction.

Random Forest:-- Random Forest is a popular supervised machine learning algorithm used for both classification and regression. It works by creating many decision trees on different parts of the data and then combining their results. This process, called ensemble learning, makes the model more accurate and reduces overfitting. In general, the more trees we add, the better the performance.