Top 350+ Solved Machine Learning (ML) MCQ Questions Answer
Q. 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?
a. step wise forward selection
b. step wise backward elimination
c. both
d. none of above
Q. Below are the two ensemble models:1. E1(M1, M2, M3) and2. E2(M4, M5, M6)Above, Mx is the individual base models.Which of the following are more likely to choose if following conditions for E1 and E2 are given?E1: Individual Models accuracies are high but models are of the same type or in another term less diverseE2: Individual Models accuracies are high but they are of different types in another term high diverse in nature
a. e1
b. e2
c. any of e1 and e2
d. none of these
Q. Which of the following is true about bagging?1. Bagging can be parallel2. The aim of bagging is to reduce bias not variance3. Bagging helps in reducing overfitting
a. 1 and 2
b. 2 and 3
c. 1 and 3
d. all of these
Q. Suppose you are using stacking with n different machine learning algorithms with k folds on data.Which of the following is true about one level (m base models + 1 stacker) stacking?Note:Here, we are working on binary classification problemAll base models are trained on all featuresYou are using k folds for base models
a. you will have only k features after the first stage
b. you will have only m features after the first stage
c. you will have k+m features after the first stage
d. you will have k*n features after the first stage
Q. Which of the following is the difference between stacking and blending?
a. stacking has less stable cv compared to blending
b. in blending, you create out of fold prediction
c. stacking is simpler than blending
d. none of these
Q. Which of the following can be one of the steps in stacking?1. Divide the training data into k folds2. Train k models on each k-1 folds and get the out of fold predictions for remaining one fold3. Divide the test data set in “k” folds and get individual fold predictions by different algorithms
a. 1 and 2
b. 2 and 3
c. 1 and 3
d. all of above
Q. Q25. Which of the following are advantages of stacking?1) More robust model2) better prediction3) Lower time of execution
a. 1 and 2
b. 2 and 3
c. 1 and 3
d. all of the above
Q. Which of the following are correct statement(s) about stacking?A machine learning model is trained on predictions of multiple machine learning modelsA Logistic regression will definitely work better in the second stage as compared to other classification methodsFirst stage models are trained on full / partial feature space of training data
a. 1 and 2
b. 2 and 3
c. 1 and 3
d. all of above
Q. Which of the following is true about weighted majority votes?1. We want to give higher weights to better performing models2. Inferior models can overrule the best model if collective weighted votes for inferior models is higher than best model3. Voting is special case of weighted voting
a. 1 and 3
b. 2 and 3
c. 1 and 2
d. 1, 2 and 3
Q. Which of the following is true about averaging ensemble?
a. it can only be used in classification problem
b. it can only be used in regression problem
c. it can be used in both classification as well as regression
d. none of these
Q. What are the two methods used for the calibration in Supervised Learning?
a. platt calibration and isotonic regression
b. statistics and informal retrieval
Q. Lets say, a Linear regression model perfectly fits the training data (train error
a. you will always have test error zero
b. b. you can not have test error zero