Random Forest

Random Forest: Random Forest is supervised learning and it is an ensemble classifier made using many decision tree models. Ensemble models combine the results from different models. The combination of learning models increases the overall result ,called bagging method. In simple words: Random forest builds multiple decision trees and merges Read more…


Entropy: It defines the randomness in the data. It helps to find out the root node,intermediate nodes and leaf node to develop the decision tree It is just a metric which measures the impurity. It reaches its minimum (zero) when all cases in the node fall into a single target Read more…

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