Top 350+ Solved Machine Learning (ML) MCQ Questions Answer
Q. We have been given a dataset with n records in which we have input attribute as x and output attribute as y. Suppose we use a linear regression method to model this data. To test our linear regressor, we split the data in training set and test set randomly. What do you expect will happen with bias and variance as you increase the size oftraining data?
a. bias increases and variance increases
b. bias decreases and variance increases
c. bias decreases and variance decreases
d. bias increases and variance decreases
Q. Suppose, you got a situation where you find that your linear regression model is under fitting the data. In such situation which of the following options would you consider?1. I will add more variables2. I will start introducing polynomial degree variables3. I will remove some variables
a. 1 and 2
b. 2 and 3
c. 1 and 3
d. 1, 2 and 3
Q. What is/are true about ridge regression?1. When lambda is 0, model works like linear regression model2. When lambda is 0, model doesn’t work like linear regression model3. When lambda goes to infinity, we get very, very small coefficients approaching 04. When lambda goes to infinity, we get very, very large coefficients approaching infinity
a. 1 and 3
b. 1 and 4
c. 2 and 3
d. 2 and 4
Q. We have been given a dataset with n records in which we have input attribute as x and output attribute as y. Suppose we use a linear regression method to model this data. To test our linear regressor, we split the data in training set and test set randomly. Now we increase the training set size gradually. As the training set size increases, what do you expect will happen with the mean training error?
a. increase
b. decrease
c. remain constant
d. can’t say
Q. We have been given a dataset with n records in which we have input attribute as x and output attribute as y. Suppose we use a linear regression method to model this data. To test our linear regressor, we split the data in training set and test set randomly. What do you expect will happen with bias and variance as you increase the size of training data?
a. bias increases and variance increases
b. bias decreasesand variance increases
c. bias decreases and variance decreases
d. bias increases and variance decreases
Q. For a multiple regression model, SST = 200 and SSE = 50. The multiple coefficient ofdetermination is
a. 0.25
b. 4.00
c. 0.75
d. none of the above
Q. A nearest neighbor approach is best used
a. with large-sized datasets.
b. when irrelevant attributes have been removed from the data.
c. when a generalized model of the data is desirable.
d. when an explanation of what has been found is of primary importance.
Q. Another name for an output attribute.
a. predictive variable
b. independent variable
c. estimated variable
d. dependent variable
Q. Classification problems are distinguished from estimation problems in that
a. classification problems require the output attribute to be numeric.
b. classification problems require the output attribute to be categorical.
c. classification problems do not allow an output attribute.
d. classification problems are designed to predict future outcome.
Q. Which statement is true about prediction problems?
a. the output attribute must be categorical.
b. the output attribute must be numeric.
c. the resultant model is designed to determine future outcomes.
d. the resultant model is designed to classify current behavior.
Q. Which of the following is a common use of unsupervised clustering?
a. detect outliers
b. determine a best set of input attributes for supervised learning
c. evaluate the likely performance of a supervised learner model
d. determine if meaningful relationships can be found in a dataset
Q. The average positive difference between computed and desired outcome values.
a. root mean squared error
b. mean squared error
c. mean absolute error
d. mean positive error
Q. Selecting data so as to assure that each class is properly represented in both the training andtest set.
a. cross validation
b. stratification
c. verification
d. bootstrapping
Q. The standard error is defined as the square root of this computation.
a. the sample variance divided by the total number of sample instances.
b. the population variance divided by the total number of sample instances.
c. the sample variance divided by the sample mean.
d. the population variance divided by the sample mean.
Q. Data used to optimize the parameter settings of a supervised learner model.
a. training
b. test
c. verification
d. validation