COMPUTER SCIENCE AND ENGINEERING
MACHINE LEARNING
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Question
[CLICK ON ANY CHOICE TO KNOW THE RIGHT ANSWER]
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The number of base estimators in the ensemble.
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The number of features
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The number of accuracies
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The number of data rows
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Detailed explanation-1: -It defines the number of decision trees to be created in a random forest. Generally, a higher number makes the predictions stronger and more stable, but a very large number can result in higher training time.
Detailed explanation-2: -Its n estimators parameter is defined as: n estimators: int, default=10 The number of base estimators in the ensemble.
Detailed explanation-3: -A Bagging classifier is an ensemble meta-estimator that fits base classifiers each on random subsets of the original dataset and then aggregate their individual predictions (either by voting or by averaging) to form a final prediction.
Detailed explanation-4: -An ensemble classifier is composed of 10 classifiers. One classifier is has an accuracy of 100% of the time in data subset X, and 0% all other times.
Detailed explanation-5: -The ensemble size is always 10 and the first ensemble members are trained using 95% of the data sampled without replacement. So the estimators are all very similar to each other. Then the next ensemble is trained using 90% of the data and so on.