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.
Question
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.
Solution
The ensemble learning technique that focuses on reducing bias is Boosting.
Here's a step-by-step explanation:
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Ensemble learning is a machine learning concept in which the idea is to train multiple models using the same learning algorithm. The ensemble method is a technique that combines predictions from multiple machine learning algorithms to make a more accurate prediction than a single model.
Similar Questions
Which ensemble learning technique is more suitable for reducing overfitting?Review LaterBaggingBoostingBoth bagging and boosting are equally effective.Neither bagging nor boosting can help reduce overfitting.
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.
Which ensemble method involves combining multiple models using weighted voting?Review LaterBaggingBoostingStackingVoting
Which of the following algorithms is not an example of ensemble learning algorithm?2 pointsRandom ForestAdaboostExtra TreesGradient BoostingDecision Trees
Which of the following machine learning algorithm is based upon the idea of bagging?Random-forestRegressionClassificationDecision treeSAVE
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