The process of using multiple data samples, generated by random sampling with replacement, from the original dataset to create multiple models and aggregating their predictions is called _______.Answer choicesSelect only one optionREVISITPruningClusteringBaggingOut of bag errors

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 method involves combining multiple models using weighted voting?Review LaterBaggingBoostingStackingVoting

Which of the following are true about bagging?Answer choicesSelect only one optionREVISITIn bagging, we choose random subsamples of the data points with replacement.In bagging, individual trees are independent of each other.Bagging helps to reduce variance, and by extension, prevents overfitting.

It is a selection of the sample randomly from a population, without any specific conditions.Group of answer choicesRandom SamplingSystematic SamplingCluster SamplingStratified Sampling

A sampling _______ is a list of elements from which a sample will be selected while sampling. biaselementerrorframe