APPLICATION OF SUPERVISED LEARNING
CLASSIFICATION IN MACHINE LEARNING
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Question
[CLICK ON ANY CHOICE TO KNOW THE RIGHT ANSWER]
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Which is true about Random Forest (RF)
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RF completely a different machine learning technique
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RF combines multiple decision trees to make a decision
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RF cannot handle continuous data
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None of the above
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Explanation:
Detailed explanation-1: -Random forest is a commonly-used machine learning algorithm trademarked by Leo Breiman and Adele Cutler, which combines the output of multiple decision trees to reach a single result. Its ease of use and flexibility have fueled its adoption, as it handles both classification and regression problems.
Detailed explanation-2: -Each node in the decision tree works on a random subset of features to calculate the output. The random forest then combines the output of individual decision trees to generate the final output. Bootstrapping is the process of randomly selecting items from the training dataset.
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