What is the difference between a Decision Tree and a Random Forest?

What is the difference between a Decision Tree and a Random Forest?

Approach: Compare a single model with an ensemble of models.

A Decision Tree is a single model prone to high variance and overfitting. A Random Forest is an ensemble of many decision trees that reduces variance by averaging the results of trees trained on different data subsets.

Follow-up: Is Random Forest harder to interpret than a single tree?

Key concepts: variance, overfitting, bagging.