What is the primary function of a Random Forest algorithm?

The primary function of a Random Forest algorithm is to construct multiple decision trees and output a mode or average of their predictions. This ensemble learning method improves the model's accuracy and robustness compared to a single decision tree. By aggregating the results from numerous trees, Random Forest mitigates overfitting, which is common in decision trees that might highly reflect the nuances of the training data. Consequently, this leads to better generalization when the model encounters new data.

Each tree in a Random Forest is trained on a subset of the data, and different subsets of features are considered at each split, which enhances diversity among the trees. The final prediction can be derived by taking a majority vote in classification tasks or averaging the results in regression tasks, which harnesses the strengths of many models to provide a more stable and accurate outcome than a single tree could achieve. This is why the option that describes the construction of multiple decision trees and the output of a mode is correct.