Answer: Ensemble methods are less likely to overfit than single decision trees

Ensemble methods, such as random forests, aggregate predictions from multiple decision trees to enhance model robustness and accuracy. The key advantage over a single decision tree is their reduced tendency to overfit. Single decision trees may become overly complex, closely fitting training data and mistaking noise for signal, which can lead to overfitting and poor performance on new data. Random forests address this by generating numerous trees, each trained on random subsets of data and features, then combining their predictions. This approach: - Boosts diversity among the trees. - Minimizes the impact of individual tree biases or variances. - Often yields a more generalized model with better performance on unseen data. Option A is incorrect because ensemble methods, particularly those with many trees like random forests, are generally seen as less interpretable than single decision trees due to the complexity of merging multiple model predictions. Option C is incorrect since ensemble methods usually require more training time than single decision trees, as they involve training multiple models. Option D is incorrect because ensemble methods, by nature of comprising multiple decision trees, demand more memory than a single tree. Thus, option B stands out as the most accurate, emphasizing the advantage of ensemble methods in mitigating overfitting, a key factor in their popularity in machine learning.

Author: LeetQuiz Editorial Team