Random Forests is a machine learning technique that uses multiple decision trees to make predictions. Each tree is trained on a random subset of the data, which helps improve accuracy and reduce overfitting. The final prediction is made by averaging the results from all the trees, making it more robust than a single decision tree. This method is widely used for classification and regression tasks due to its ability to handle large datasets and maintain high performance. Random Forests can also provide insights into feature importance, helping to identify which variables are most influential in making predictions.