MACHINE LEARNING

Detailed explanation-1: -The Random Forest Algorithm combines the output of multiple (randomly created) Decision Trees to generate the final output. This process of combining the output of multiple individual models (also known as weak learners) is called Ensemble Learning.

Detailed explanation-2: -For classification tasks, the output of the random forest is the class selected by most trees. For regression tasks, the mean or average prediction of the individual trees is returned. Random decision forests correct for decision trees’ habit of overfitting to their training set.

Detailed explanation-3: -Random Forest is a powerful and versatile supervised machine learning algorithm that grows and combines multiple decision trees to create a “forest.” It can be used for both classification and regression problems in R and Python.

Detailed explanation-4: -A random forest is simply a collection of decision trees whose results are aggregated into one final result. Their ability to limit overfitting without substantially increasing error due to bias is why they are such powerful models. One way Random Forests reduce variance is by training on different samples of the data.