Helpful examples of using Random Forest (RF) machine learning algorithms in scikit-learn.
The Random Forest algorithm is an ensemble learning method used for classification and regression tasks.
It builds multiple decision trees during training and merges their results to improve accuracy and control overfitting. Each tree in the forest is trained on a randomly selected subset of the data, with a random subset of features considered for splitting at each node.
This randomness helps create diverse trees, which collectively reduce variance and bias, leading to a more robust model.
In classification, the final output is determined by majority voting among the trees, while in regression, the mean prediction of the trees is used.
Random Forests are highly effective, scalable, and handle large datasets with higher accuracy and stability.