APPLICATION OF SUPERVISED LEARNING
DEEP LEARNING
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Question
[CLICK ON ANY CHOICE TO KNOW THE RIGHT ANSWER]
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Different learners can come from same algorithm with different hyper parameters
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Different learners can come from different algorithms
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Different learners can come from different training spaces
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All of these
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Detailed explanation-1: -Q4) Which of the following can be true for selecting base learners for an ensemble? We can create an ensemble by following any / all of the options mentioned above. So option D is correct.
Detailed explanation-2: -Base learners are usually generated from training data by a base learning algorithm which can be decision tree, neural network or other kinds of machine learning algorithms.
Detailed explanation-3: -The most popular ensemble methods are boosting, bagging, and stacking. Ensemble methods are ideal for regression and classification, where they reduce bias and variance to boost the accuracy of models.
Detailed explanation-4: -Stacking is an ensemble learning technique that uses predictions from multiple models (for example decision tree, knn or svm) to build a new model. This model is used for making predictions on the test set.