Random forest

Here are the key points about random forest algorithms:

• Random forests are an ensemble supervised learning method used for both classification and regression.
• They operate by constructing a multitude of decision trees during training and outputting the class that is the mode of the classes or mean prediction of the individual trees.
• The algorithm introduces randomness when growing trees by subsampling training data samples and features to add diversity between trees.
• Combining multiple decision trees via bagging and feature randomness helps avoid overfitting and improves generalization.
• Random forests generally have high accuracy for many problems and can handle both categorical and continuous data.
• They can model complex nonlinear relationships between features and output. The ensemble of trees can capture complex interactions.
• Tuning parameters include the number of trees, max tree depth, and number of features subsampled per tree.
• Limitations include lower interpretability compared to individual decision trees and longer computational training time.
• Random forests are used for tasks like classification, regression, feature selection and are commonly implemented in data science pipelines.

Random forests use an ensemble of randomized decision trees to create accurate predictions while avoiding overfitting, making them a versatile machine learning technique.