Top 350+ Solved Machine Learning (ML) MCQ Questions Answer
Q. A feature F1 can take certain value: A, B, C, D, E, & F and represents grade of students from a college.Which of the following statement is true in following case?
a. Feature F1 is an example of nominal variable.
b. Feature F1 is an example of ordinal variable.
c. It doesn’t belong to any of the above category.
d. Both of these
Q. The parameter______ allows specifying the percentage of elements to put into the test/training set
a. test_size
b. training_size
c. All above
d. None of these
Q. In many classification problems, the target ______ is made up of categorical labels which cannot immediately be processed by any algorithm.
a. random_state
b. dataset
c. test_size
d. All above
Q. _______adopts a dictionary-oriented approach, associating to each category label a progressive integer number.
a. LabelEncoder class
b. LabelBinarizer class
c. DictVectorizer
d. FeatureHasher
Q. If Linear regression model perfectly first i.e., train error is zero, then _____________________
a. a) Test error is also always zero
b. b) Test error is non zero
c. c) Couldn’t comment on Test error
d. d) Test error is equal to Train error
Q. Function used for linear regression in R is __________
a. a) lm(formula, data)
b. b) lr(formula, data)
c. c) lrm(formula, data)
d. d) regression.linear(formula, data)
Q. In syntax of linear model lm(formula,data,..), data refers to ______
a. a) Matrix
b. b) Vector
c. c) Array
d. d) List
Q. Which of the following methods do we use to find the best fit line for data in Linear Regression?
a. A) Least Square Error
b. B) Maximum Likelihood
c. C) Logarithmic Loss
d. D) Both A and B
Q. Which of the following evaluation metrics can be used to evaluate a model while modeling a continuous output variable?
a. A) AUC-ROC
b. B) Accuracy
c. C) Logloss
d. D) Mean-Squared-Error
Q. Which of the following is true about Residuals ?
a. A) Lower is better
b. B) Higher is better
c. C) A or B depend on the situation
d. D) None of these
Q. Which of the following statement is true about outliers in Linear regression?
a. A) Linear regression is sensitive to outliers
b. B) Linear regression is not sensitive to outliers
c. C) Can’t say
d. D) None of these
Q. Suppose you plotted a scatter plot between the residuals and predicted values in linear regression and you found that there is a relationship between them. Which of the following conclusion do you make about this situation?
a. A) Since the there is a relationship means our model is not good
b. B) Since the there is a relationship means our model is good
c. C) Can’t say
d. D) None of these
Q. Naive Bayes classifiers are a collection ------------------of algorithms
a. Classification
b. Clustering
c. Regression
d. All
Q. Naive Bayes classifiers is _______________ Learning
a. Supervised
b. Unsupervised
c. Both
d. None
Q. A supervised scenario is characterized by the concept of a _____.
a. Programmer
b. Teacher
c. Author
d. Farmer