COMPUTER SCIENCE AND ENGINEERING
MACHINE LEARNING
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Question
[CLICK ON ANY CHOICE TO KNOW THE RIGHT ANSWER]
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S1 and S2 are correct, and S2 is correct explanation of S1
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S1 and S2 are correct, and S2 is not the correct explanation of S1
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S1 is correct and S2 are incorrect
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S1 is correct and S2 are correct
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Detailed explanation-1: -11.2 Extending bagging However, as we saw in Section 10.6, simply bagging trees results in tree correlation that limits the effect of variance reduction. Random forests help to reduce tree correlation by injecting more randomness into the tree-growing process.
Detailed explanation-2: -QUESTION 1 Which of the following statements is true about random forests Random forests are an ensemble method. They combine and average the predictions from large number of trees.
Detailed explanation-3: -Random forests are said to reduce variance in relation to bagging trees, because of its random selection of features-it reduces correlation between trees.
Detailed explanation-4: -Random forest is less likely to overfit the data than decision tree. This is because each individual model in random forest is trained on a random subset of the data, which reduces the chance that the model will learn from noise rather than signal.