試験Databricks-Machine-Learning-Associate トピック2 問題19 スレッド

Random Forest is a machine learning algorithm that typically uses bagging (Bootstrap Aggregating). Bagging involves training multiple models independently on different random subsets of the data and then combining their predictions. Random Forests consist of many decision trees trained on random subsets of the training data and features, and their predictions are averaged to improve accuracy and control overfitting. This method enhances model robustness and predictive performance.
Reference:
Ensemble Methods in Machine Learning (Understanding Bagging and Random Forests).