Learn how random forest , a popular ensemble method, combines multiple decision trees to make predictions for classification and regression tasks. Understand its working, features, advantages, disadvantages, and coding in Python with examples. Learn how to use a random forest classifier, a meta estimator that fits a number of decision tree classifiers on various sub-samples of the dataset and uses averaging to improve the predictive accuracy and control over-fitting. See the parameters, attributes and examples of this class. Random Forest is a part of bagging (bootstrap aggregating) algorithm because it builds each tree using different random part of data and combines their answers together. Learn from this step-by-step random forest example using Python. Ideal for beginners, this guide explains how to use the random forest ..
