Random Forests Algorithm Concepts of Random forests Random forests are an ensemble learning method that combines multiple decision trees to create a more accurate and robust model. In a random forest, multiple decision trees are trained on random subsets of the data and features, and the final prediction is made by averaging the predictions of the individual trees. For example, let's say we have a dataset of customer information, including age, income, education level, and purchase history. We can use a random forest to predict whether a customer will make a purchase based on these attributes. Random forests  Algorithm Define the problem and collect data. Choose a hypothesis class (e.g., random forests). Split the data into training and validation sets. Construct multiple decision trees using random subsets of the data and features. Aggregate the predictions from all the trees to make a final prediction. Evaluate the model on the validation set to estimate its performance. Apply th