Related Questions:
– What is a Random Forest?
– What is the difference between Decision Trees, Bagging and Random Forest?
Random forest is an ensemble learning model that uses multiple decision trees to make predictions. It is a popular supervised learning algorithm that can be used for both classification and regression tasks.
During training, the algorithm creates a large number of decision trees by randomly sampling the data and features. Each tree is trained on a subset of the data and makes a decision based on a random subset of the features. Once all the trees are trained, the final prediction is made by aggregating the outputs of all the individual trees.
Here are some advantages and disadvantages of using random forest:
Advantages of Random Forest
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Disadvantages of Random Forest
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