Random Forest is a versatile machine learning algorithm that operates by constructing multiple decision trees during training and aggregating their outputs to produce a final prediction. This ensemble approach enhances predictive accuracy and mitigates the risk of overfitting, a common issue with individual decision trees.
How It Works:
- Data Sampling: Random Forest employs bootstrapping, a technique where multiple subsets of the training data are created by sampling with replacement.
- Tree Construction: For each data subset, a decision tree is built. During this process, at each node, a random subset of features is considered for splitting, introducing diversity among the trees.
- Aggregation: For classification tasks, each tree votes on the class label, and the majority vote determines the final prediction. For regression tasks, the average of all tree predictions is taken as the final output.