Preface: Random forest(RF) is a supervised machine learning algorithm. It is flexible and easy to use which returns good accuracy even without proper hyper-parameter tuning. The term “random-forest” is a compilation of two different terms that is “random”, which comes from Bagging. Bagging is a random selection of features from a dataset. The term “forest” says that a group of decision trees working together to predict a target variable. The random forest fall in the category of ensemble learning. How random forest works: Random forest trains several decision trees in parallel. Let say we have a dataset with 10 features
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