DATABASE FUNDAMENTALS
BASICS OF BIG DATA
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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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2 and 3
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1 and 3
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1, 2 and 3
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Detailed explanation-1: -Both options are true. In Bagging, each individual trees are independent of each other because they consider different subset of features and samples.
Detailed explanation-2: -Bagging regression trees Bagging on decision trees is done by creating bootstrap samples from the training data set and then built trees on bootstrap samples and then aggregating the output from all the trees and predicting the output.
Detailed explanation-3: -11. Which of the following is a widely used and effective machine learning algorithm based on the idea of bagging? Explanation: The Radom Forest algorithm builds an ensemble of Decision Trees, mostly trained with the bagging method.
Detailed explanation-4: -Which of the following machine learning algorithm is based upon the idea of bagging? Answer-B) Random forest is based on the idea of bagging.
Detailed explanation-5: -Bagging is a technique of building many decision tree models at a time by randomly sampling with replacement, or bootstrapping, from the original dataset. This ensures variety in the trees, which helps to reduce the amount of overfitting. Random forest models take this concept one step further.