Top 350+ Solved Machine Learning (ML) MCQ Questions Answer
Q. Which one of the following is suitable? 1. When the hypothsis space is richer, overfitting is more likely. 2. when the feature space is larger , overfitting is more likely.
a. true, false
b. false, true
c. true,true
d. false,false
Q. Which of the following is a categorical data?
a. branch of bank
b. expenditure in rupees
c. prize of house
d. weight of a person
Q. The soft margin SVM is more preferred than the hard-margin SVM when-
a. the data is linearly seperable
b. the data is noisy and contains overlapping points
c. the data is not noisy and linearly seperable
d. the data is noisy and linearly seperable
Q. In SVM which has quadratic kernel function of polynomial degree 2 that has slack variable C as one hyper paramenter. What would happen if we use very large value for C
a. we can still classify the data correctly for given setting of hyper parameter c
b. we can not classify the data correctly for given setting of hyper parameter c
c. we can not classify the data at all
d. data can be classified correctly without any impact of c
Q. In SVM, RBF kernel with appropriate parameters to perform binary classification where the data is non-linearly seperable. In this scenario
a. the decision boundry in the transformed feature space in non-linear
b. the decision boundry in the transformed feature space in linear
c. the decision boundry in the original feature space in not considered
d. the decision boundry in the original feature space in linear
Q. Which of the following is true about SVM? 1. Kernel function map low dimensional data to high dimensional space. 2. It is a similarity Function
a. 1 is true, 2 is false
b. 1 is false, 2 is true
c. 1 is true, 2 is true
d. 1 is false, 2 is false
Q. Which of the following method is used for multiclass classification?
a. one vs rest
b. loocv
c. all vs one
d. one vs another
Q. MLE estimates are often undesirable because
a. they are biased
b. they have high variance
c. they are not consistent estimators
d. none of the above
Q. The difference between the actual Y value and the predicted Y value found using a regression equation is called the
a. slope
b. residual
c. outlier
d. scatter plot
Q. Neural networks
a. optimize a convex cost function
b. always output values between 0 and 1
c. can be used for regression as well as classification
d. all of the above
Q. Linear Regression is a _______ machine learning algorithm.
a. supervised
b. unsupervised
c. semi-supervised
d. can\t say