True or False: Dropout is computationally expensive technique w.r.t. bagging

Related MCQs

• Which of the following is true about bagging? 1. Bagging can be parallel 2. The aim of bagging is to reduce bias not variance 3. Bagging helps in reducing overfitting
• In machine learning, an algorithm (or learning algorithm) is said to be unstable if a small change in training data cause the large change in the learned classifiers. True or False: Bagging of unstable classifiers is a good idea
• To control the size of the tree, we need to control the number of regions. One approach to do this would be to split tree nodes only if the resultant decrease in the sum of squares error exceeds some threshold. For the described method, which among the following are true? (a) It would, in general, help restrict the size of the trees (b) It has the potential to affect the performance of the resultant regression/classification model (c) It is computationally infeasible
• How is the model capacity affected with dropout rate (where model capacity means the ability of a neural network to approximate complex functions)?
• Which of the following parameters can be tuned for finding good ensemble model in bagging based algorithms? 1. Max number of samples 2. Max features 3. Bootstrapping of samples 4. Bootstrapping of features
• Which of the following parameters can be tuned for finding good ensemble model in bagging based algorithms? 1. Max number of samples 2. Max features 3. Bootstrapping of samples 4. Bootstrapping of features
• True or False: Ensemble learning can only be applied to supervised learning methods.
• True or False: Ensembles will yield bad results when there is significant diversity among the models. Note: All individual models have meaningful and good predictions.
• True or False: Ensemble of classifiers may or may not be more accurate than any of its individual model.
• True or False: In boosting, individual base learners can be parallel.