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.

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.

Which ensemble method involves combining multiple models using weighted voting?Review LaterBaggingBoostingStackingVoting

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 of the following machine learning algorithm is based upon the idea of bagging?Random-forestRegressionClassificationDecision treeSAVE

Question No. 4Marks : 1.00    Bagging speeds up model training by parallelizing computation, while boosting reduces overfitting by adding regularization           Bagging creates multiple datasets by sampling with replacement, while boosting adds models sequentially and adjusts their weights based on the error of the previous models           Bagging