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Machine Learning (ML) MCQs: 623 Questions & Answers in Engineering

623 Machine Learning (ML) MCQs in Engineering with answers & explanations. Page 13. Practice objective questions for competitive exams, interviews & self-ass… Use the answer toggle below to show or hide all solutions, tap an option to check one question, or open a question for its own page.

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Machine Learning (ML) MCQs: 623 Questions & Answers in Engineering

The F-test

What is true about an ensembled classifier? 1. Classifiers that are more “sure” can vote with more conviction 2. Classifiers can be more “sure” about a particular part of the space 3. Most of the times, it performs better than a single classifier

Which of the following option is / are correct regarding benefits of ensemble model? 1. Better performance 2. Generalized models 3. Better interpretability

Which of the following can be true for selecting base learners for an ensemble? 1. Different learners can come from same algorithm with different hyper parameters 2. Different learners can come from different algorithms 3. Different learners can come from different training spaces

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.

Which of the following is / are true about weak learners used in ensemble model? 1. They have low variance and they don’t usually overfit 2. They have high bias, so they can not solve hard learning problems 3. They have high variance and they don’t usually overfit

True or False: Ensemble of classifiers may or may not be more accurate than any of its individual model.

Generally, an ensemble method works better, if the individual base models have ____________? Note: Suppose each individual base models have accuracy greater than 50%.

Suppose there are 25 base classifiers. Each classifier has error rates of e = 0.35. Suppose you are using averaging as ensemble technique. What will be the probabilities that ensemble of above 25 classifiers will make a wrong prediction? Note: All classifiers are independent of each other

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

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

How is the model capacity affected with dropout rate (where model capacity means the ability of a neural network to approximate complex functions)?

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

Suppose, you have 2000 different models with their predictions and want to ensemble predictions of best x models. Now, which of the following can be a possible method to select the best x models for an ensemble?

True or False: In boosting, individual base learners can be parallel.

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

Study tips for Machine Learning (ML)