APPLICATION OF SUPERVISED LEARNING
SUPERVISED 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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Adaboost
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Decision Trees
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Gradient Boosting
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Detailed explanation-1: -The algorithm that is not an example of an ensemble learning algorithm is d) Decision Trees. Decision Trees are not an example of ensemble learning because they represent a single tree structure that makes a decision based on a set of rules learned from the training data.
Detailed explanation-2: -Bagging, boosting and random forests are ensemble learning algorithms. Their common property is that they generate ensembles of base classifiers and ensure their diversity by providing them with different sets of learning examples. Bagging. The term bagging is short for bootstrap aggregating.
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.