Can you explain how a Random Forest algorithm differs from a Decision Tree?

A Random Forest algorithm differs from a Decision Tree in that it creates an ensemble of multiple decision trees to make predictions, rather than relying on a single tree.

Key differences:

1. Model Complexity: Random Forest aggregates many decision trees, reducing overfitting and improving accuracy, while a single Decision Tree can overfit the training data.
2. Diversity: Random Forest introduces randomness by selecting subsets of features and data for each tree, leading to more robust and generalized predictions.
3. Error Reduction: By averaging the predictions of multiple trees, Random Forest generally achieves lower variance and better performance compared to a single Decision Tree.