Random forests is a machine learning method that combine multiple decision trees to improve prediction accuracy and reduce overfitting.

The basic idea behind random forests is to generate a large number of decision trees, each one trained on a different subset of the data and features.

For each split in the decision tree, a random subset of features is selected, rather than using all available features.

The final prediction is made by aggregating the predictions of all the decision trees in the forest

Random forests are less prone to overfitting than single decision trees

Random forests are also robust to noisy data and can handle missing values.

They are computationally efficient and can be trained in parallel.

Hyperparameters such as the number of trees in the forest and the maximum depth of each tree can be tuned to optimize performance.