What does a Random Forest algorithm do?

The Random Forest algorithm is a popular machine learning technique that enhances predictive accuracy and helps in tasks like classification and regression by utilizing an ensemble approach. This method works by constructing multiple decision trees during training and merging their outcomes to produce a more reliable and accurate prediction.

Each individual tree is trained on a random subset of the data and may also use a random subset of features when making splits. This randomness helps to promote diversity among the trees, thus reducing the risk of overfitting that can occur when relying on a single decision tree. When it comes time to make a prediction, the Random Forest takes the majority vote (for classification tasks) or averages the predictions (for regression tasks) from all the trees to arrive at a final outcome. This approach generally leads to improved performance compared to a single decision tree, making it a preferred choice in various data science and machine learning applications.