Random forest, a popular machine learning algorithm developed by Leo Breiman and Adele Cutler, merges the outputs of numerous decision trees to produce a single outcome. Its popularity stems from its user-friendliness and versatility, making it suitable for both classification and regression tasks.
All you need to know about the random forest model in machine learning. Random forest is a flexible, easy-to-use machine learning algorithm that produces, even without hyperparameter tuning, a great result most of the time.
The Random Forest algorithm is one of the most popular and best-performing machine learning algorithms available today. Random forests are an ensemble learning technique that works by constructing multiple decision trees with diverse samples, thereby helping to build a more accurate and robust model.
A Random Forest Algorithm is a supervised machine learning algorithm that is extremely popular and is used for Classification and Regression problems in Machine Learning.