The paper “Random Forests” by Leo Breiman, published in 2001, introduces the Random Forest algorithm, a powerful ensemble learning method for classification and regression tasks. The algorithm constructs multiple decision trees using random subsets of the training data and random subsets of the features. Each tree independently classifies or predicts the target variable, and the final prediction is obtained by aggregating the outputs from all the trees. Random Forests reduce overfitting and improve predictive accuracy by combining the predictions of diverse and decorrelated trees. The method is known for its versatility, robustness, and ability to handle high-dimensional data, making it a popular and widely-used tool in machine learning and data science.