Random Forest (Easily Explained)
Random Forest (Easily Explained) - (With Python implementation in depth!) R andom Forest is an ensemble technique which can be used for both regression and classification tasks. An ensemble method is a technique that combines the predictions from multiple machine learning algorithms together to make more accurate predictions than any individual model. Random Forests basically combine the simplicity of decision tree with flexibility resulting in a vast improvement in accuracy. It is also called “Bagging”(Bootstrap Aggregation) and the main goal of the Random Forest is to reduce the variance of the decision tree. Low Bias and High Variance: Over-fitting (this is where we use Random Forest to minimize the variance by splitting the data into chunks of features/data and train it). Random Forest is used when our goal is to reduce the variance of a decision tree. Here idea is to create several subsets of data from the training samples chosen randomly with replacemen
