APPLICATION OF SUPERVISED LEARNING
DEEP LEARNING
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Question
[CLICK ON ANY CHOICE TO KNOW THE RIGHT ANSWER]
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Random Forest
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Gradient Boosting
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Decision Tree
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None of these
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Detailed explanation-1: -Q1. Which of the following algorithm is not an example of an ensemble method? Option D is correct. In case of decision tree, we build a single tree and no ensembling is required.
Detailed explanation-2: -An example of an ensemble learning algorithm is bagging [2]. Given a learning algorithm for creating single predictive models and a data set, bagging creates diverse predictive models by feeding different uniform samples of the data set to the learning algorithm in order to create each model.
Detailed explanation-3: -Ensemble methods, which combines several decision trees to produce better predictive performance than utilizing a single decision tree. The main principle behind the ensemble model is that a group of weak learners come together to form a strong learner.
Detailed explanation-4: -Ensemble Methods Based on Decision Trees Random Forest (Regressor / Classifier) Extremely Randomized Trees (Regressor / Classifier) Bagging (Regressor / Classifier)