![]() It can be used for both Classification and Regression problems in ML. It is a group of decision trees combined together to give output.Random Forest is a popular machine learning algorithm that belongs to the supervised learning technique. It is a tree-like decision-making diagram. Therefore this process is a slow process and can sometimes take hours or even days. In the forest, we need to generate, process and analyze each and every tree. More trees will give a more robust model and prevents overfitting. A decision tree is easy to read and understand whereas random forest is more complicated to interpret.Ī single decision tree is not accurate in predicting the results but is fast to implement. Using multiple trees in the random forest reduces the chances of overfitting. Random Forest is the collection of decision trees with a single and aggregated result. Since it has multiple decision trees, therefore, is slow in generating predictions.ĭecision trees are simple but suffer from some serious problems- overfitting, error due to variance or error due to bias.
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