COMPUTER SCIENCE AND ENGINEERING
MACHINE LEARNING
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Question
[CLICK ON ANY CHOICE TO KNOW THE RIGHT ANSWER]
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1 and 2
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1 and 3
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2 and 3
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All of the above
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Detailed explanation-1: -What is true about an ensembled classifier? In an ensemble model, we give higher weights to classifiers which have higher accuracies. In other words, these classifiers are voting with higher conviction. On the other hand, weak learners are sure about specific areas of the problem.
Detailed explanation-2: -1. Which of the following is/are true about ensemble methods? Ensemble methods can take the form of using different algorithms, using the same algorithm with different settings, or assigning different parts of the dataset to different classifiers.
Detailed explanation-3: -Ensemble learning is used when you build component classifiers that are more accurate and independent of each other. True Positive is when the predicted instance and the actual instance are positive. Ensemble learning is used when you build component classifiers that are more accurate and independent of each other.
Detailed explanation-4: -Ensemble learning is a way of generating various base classifiers from which a new classifier is derived which performs better than any constituent classifier . These base classifiers may differ in the algorithm used, hyperparameters, representation or the training set.
Detailed explanation-5: -In classification, a hard voting ensemble involves summing the votes for crisp class labels from other models and predicting the class with the most votes. A soft voting ensemble involves summing the predicted probabilities for class labels and predicting the class label with the largest sum probability.