Top 350+ Solved Machine Learning (ML) MCQ Questions Answer
Q. How does the bias-variance decomposition of a ridge regression estimator compare with that of ordinaryleast squares regression?
a. ridge has larger bias, larger variance
b. ridge has smaller bias, larger variance
c. ridge has larger bias, smaller variance
d. ridge has smaller bias, smaller variance
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. How can SVM be classified?
a. it is a model trained using unsupervised learning. it can be used for classification and regression.
b. it is a model trained using unsupervised learning. it can be used for classification but not for regression.
c. it is a model trained using supervised learning. it can be used for classification and regression.
d. t is a model trained using unsupervised learning. it can be used for classification but not for regression.
Q. Which of the following can help to reduce overfitting in an SVM classifier?
a. use of slack variables
b. high-degree polynomial features
c. normalizing the data
d. setting a very low learning rate
Q. Suppose you have trained an SVM with linear decision boundary after training SVM, you correctly infer that your SVM model is under fitting. Which of the following is best option would you more likely to consider iterating SVM next time?
a. you want to increase your data points
b. you want to decrease your data points
c. you will try to calculate more variables
d. you will try to reduce the features
Q. What is/are true about kernel in SVM? 1. Kernel function map low dimensional data to high dimensional space2. It’s a similarity function
a. 1
b. 2
c. 1 and 2
d. none of these
Q. You trained a binary classifier model which gives very high accuracy on the training data, but much lower accuracy on validation data. Which is false.
a. this is an instance of overfitting
b. this is an instance of underfitting
c. the training was not well regularized
d. the training and testing examples are sampled from different distributions
Q. Suppose your model is demonstrating high variance across the different training sets. Which of the following is NOT valid way to try and reduce the variance?
a. increase the amount of traning data in each traning set
b. improve the optimization algorithm being used for error minimization.
c. decrease the model complexity
d. reduce the noise in the training data
Q. Suppose you are using RBF kernel in SVM with high Gamma value. What does this signify?
a. the model would consider even far away points from hyperplane for modeling
b. the model would consider only the points close to the hyperplane for modeling
c. the model would not be affected by distance of points from hyperplane for modeling
d. none of the above
Q. Wrapper methods are hyper-parameter selection methods that
a. should be used whenever possible because they are computationally efficient
b. should be avoided unless there are no other options because they are always prone to overfitting.
c. are useful mainly when the learning machines are “black boxes”
d. should be avoided altogether.
Q. Which of the following methods can not achieve zero training error on any linearly separable dataset?
a. decision tree
b. 15-nearest neighbors
c. hard-margin svm
d. perceptron
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. The minimum time complexity for training an SVM is O(n2). According to this fact, what sizes of datasets are not best suited for SVM’s?
a. Large datasets
b. Small datasets
c. Medium sized datasets
d. Size does not matter
Q. The effectiveness of an SVM depends upon:
a. Selection of Kernel
b. Kernel Parameters
c. Soft Margin Parameter C
d. All of the above