Machine learning and statistics with python

Introduction: Random forest is one of the highest used models in classical machine learning. Because of its robustness in high noisy data, and its much better ability to learn irregular patterns of data makes the random forest a worthy candidate for modeling in many fields like genomics and others. In this post, we will discuss: definition of a random forest What is the meaning of bagging What do you mean by ensemble model What is a decision tree The mathematics behind decision tree Training a Random forest Random forest packages Hyperparameter tuning Random forest How many trees should there be in Random forest Further study links definition of a random forest "Random forests are a combination of tree predictors such that each tree depends on the values of a random vector sampled independently and with the same distribution for all trees in the forest." --Leo Breiman Random forest is an ensemble model consisting of number of decision trees...