How does the Random Forest algorithm enhance predictive accuracy?

The Random Forest algorithm enhances predictive accuracy by aggregating the outputs of multiple decision trees. This ensemble method works by constructing numerous individual decision trees during training and then combining their predictions to make a final decision. Each tree in the forest operates independently on a bootstrap sample of the data, which introduces diversity and helps to reduce overfitting—a common issue where a model learns noise in the training data instead of general patterns.

By averaging the predictions (in the case of regression) or using majority voting (in the case of classification) among the various trees, Random Forest achieves improved accuracy and robustness compared to a single decision tree. This ensemble approach helps mitigate the weaknesses of any individual tree, leading to a more reliable and generalized model that performs better on unseen data.

Other options, such as relying on a single decision tree, deny the inherent variability that can come from using multiple trees and consequently restrict the model's ability to capture complex patterns. Eliminating irrelevant features or minimizing the dataset size may contribute to a model’s efficiency but do not directly explain the predictive accuracy enhancement characteristic of the Random Forest approach.