Which of the following machine learning algorithm is based upon the idea of bagging?
Question
Which of the following machine learning algorithm is based upon the idea of bagging?
Solution
The machine learning algorithm that is based on the idea of bagging is the Random Forest algorithm.
Here are the steps explaining how it works:
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Random Subsets: The Random Forest algorithm starts by creating random subsets from the original dataset.
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Decision Trees: For each subset, the algorithm creates a decision tree. The decision tree is created by using the best attributes from the dataset. The best attribute is chosen by calculating the information gain for each attribute.
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Voting: Once all the decision trees are created, each tree votes for a class to predict the outcome.
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Final Prediction: The class that gets the most votes is chosen as the final prediction.
So, in summary, the Random Forest algorithm is based on the idea of bagging as it creates multiple subsets of the original dataset (bootstrap samples), builds a decision tree for each of these samples, and then makes a final prediction based on the majority voting system.
Similar Questions
Which of the following machine learning algorithm is based upon the idea of bagging?Random-forestRegressionClassificationDecision treeSAVE
What is the purpose of bagging in machine learning?Review LaterTo reduce bias in the model's predictions.To increase the complexity of weak learners.To improve the stability and accuracy of the model.To reduce the computational complexity of the training process.
It has been recommended to use bagging to solve a given regression problem. Which of the following indicate the advantage of using bagging for the same instead of a simple learning algorithm?Select an option Clear ResponseLesser complexityMore accurate predictionFaster executionThe requirement of the smaller training dataset
Machine Learning Methods
Which ensemble learning technique focuses on reducing bias?Review LaterBaggingBoostingBoth bagging and boosting equally focus on reducing bias.Neither bagging nor boosting focuses on reducing bias.
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