Top 350+ Solved Machine Learning (ML) MCQ Questions Answer
Q. ______ showed better performance than other approaches, even without a context-based model
a. Machine learning
b. Deep learning
c. Reinforcement learning
d. Supervised learning
Q. Suppose we fit “Lasso Regression” to a data set, which has 100 features (X1,X2…X100). Now, we rescale one of these feature by multiplying with 10 (say that feature is X1), and then refit Lasso regression with the same regularization parameter.Now, which of the following option will be correct?
a. It is more likely for X1 to be excluded from the model
b. It is more likely for X1 to be included in the model
c. Can’t say
d. None of these
Q. If Linear regression model perfectly first i.e., train error is zero, then _____________________
a. Test error is also always zero
b. Test error is non zero
c. Couldn’t comment on Test error
d. Test error is equal to Train error
Q. Which of the following metrics can be used for evaluating regression models?i) R Squaredii) Adjusted R Squarediii) F Statisticsiv) RMSE / MSE / MAE
a. ii and iv
b. i and ii
c. ii, iii and iv
d. i, ii, iii and iv
Q. In syntax of linear model lm(formula,data,..), data refers to ______
a. Matrix
b. Vector
c. Array
d. List
Q. We can also compute the coefficient of linear regression with the help of an analytical method called “Normal Equation”. Which of the following is/are true about “Normal Equation”?1. We don’t have to choose the learning rate2. It becomes slow when number of features is very large3. No need to iterate
a. 1 and 2
b. 1 and 3.
c. 2 and 3.
d. 1,2 and 3.
Q. Which of the following option is true regarding “Regression” and “Correlation” ?Note: y is dependent variable and x is independent variable.
a. The relationship is symmetric between x and y in both.
b. The relationship is not symmetric between x and y in both.
c. The relationship is not symmetric between x and y in case of correlation but in case of regression it is symmetri
d. The relationship is symmetric between x and y in case of correlation but in case of regression it is not symmetric.
Q. Which of the following are real world applications of the SVM?
a. Text and Hypertext Categorization
b. Image Classification
c. Clustering of News Articles
d. All of the above
Q. Let’s say, you are working with categorical feature(s) and you have not looked at the distribution of the categorical variable in the test data.You want to apply one hot encoding (OHE) on the categorical feature(s). What challenges you may face if you have applied OHE on a categorical variable of train dataset?
a. All categories of categorical variable are not present in the test dataset.
b. Frequency distribution of categories is different in train as compared to the test dataset.
c. Train and Test always have same distribution.
d. Both A and B
Q. _____which can accept a NumPy RandomState generator or an integer seed.
a. make_blobs
b. random_state
c. test_size
d. training_size
Q. ______is the most drastic one and should be considered only when the dataset is quite large, the number of missing features is high, and any prediction could be risky.
a. Removing the whole line
b. Creating sub-model to predict those features
c. Using an automatic strategy to input them according to the other known values
d. All above
Q. It's possible to specify if the scaling process must include both mean and standard deviation using the parameters________.
a. with_mean=True/False
b. with_std=True/False
c. Both A & B
d. None of the Mentioned
Q. Suppose you have fitted a complex regression model on a dataset. Now, you are using Ridge regression with tuning parameter lambda to reduce its complexity. Choose the option(s) below which describes relationship of bias and variance with lambda.
a. In case of very large lambda; bias is low, variance is low
b. In case of very large lambda; bias is low, variance is high
c. In case of very large lambda; bias is high, variance is low
d. In case of very large lambda; bias is high, variance is high