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

boosting = 'train the algorithm sequentially , where each subsequent algorithm used the previous algorithm output as input 'bagging = 'combined the output of multiple or same algorithm, and used the different random data subset in the training data , also the sampling must be performed with replacement '

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.

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

Which of the following is a disadvantage of boosting algorithms?Review LaterBoosting can lead to underfittingBoosting can be computationally expensiveBoosting requires a large number of hyperparametersBoosting is not suitable for imbalanced data