Random Forest Algorithm

The Random Forest Algorithm is a form of ensemble learning that combines multiple decision trees. Each decision tree asks a series of questions to lead to a classification answer. Decision trees can capture non-linear relationships by splitting data based on different variables such as age or employment status. However, decision trees are prone to overfitting if they become too deep and complex. Bagging is an important concept in the Random Forest Algorithm, which involves creating multiple random subsets of the training data and training models on each subset. The models are then combined by either averaging their predictions or using voting to make predictions on new data. Random forest takes the concept of bagging further by also using feature bagging, where only a subset of available features is used for each decision tree in the ensemble. This algorithm is advantageous when dealing with stochastic data and can effectively capture different customer preferences in scenarios such as recommending books in a bookstore.