Advanced Database Management System - Tutorials and Notes: Machine Learning Multiple Choice Questions and Answers 13

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Wednesday, 9 September 2020

Machine Learning Multiple Choice Questions and Answers 13

Top 3 Machine Learning Quiz Questions with Answers explanation, Interview questions on machine learning, quiz questions for data scientist answers explained, machine learning exam questions, SVM

Machine learning MCQ - Set 13


1. Which of the following methods can achieve zero training error on any linearly separable dataset?

a) Decision tree

b) 15-nearest neighbors

c) Perceptron

d) Logistic regression

Answer: (a) Decision tree (b) Perceptron

Decision tree – Standard decision trees are having no learning biased. The training set error is always zero in decision trees if there is no label noise.

Perceptron - Since the data set is linearly separable, any subset of the data is also linearly separable. Thus, the perceptron is guaranteed to converge to a perfect solution on the training set. This may not be always true for testing dataset.


2. Consider a point that is correctly classified and distant from the decision boundary. Which of the following methods will be unaffected by this point?

a) Nearest neighbor

b) SVM

c) Logistic regression

d) Linear regression

Answer: (b) SVM

The hinge loss used by SVMs gives zero weight to these points. Hence, they are unaffected by this point. Whereas, the log-loss used by logistic regression gives a little bit of weight to these points.


3. Suppose your model is overfitting. Which of the following is NOT a valid way to try and reduce the overfitting?

a) Increase the amount of training data.

b) Improve the optimization algorithm being used for error minimization.

c) Decrease the model complexity.

d) Reduce the noise in the training data.

Answer: (b) Improve the optimization algorithm being used for error minimization.

Increase the amount of training data that are noisy would help in reducing overfit problem.

Increased complexity of the underlying model may increase the overfitting problem. Decreasing the complexity may help in reducing the overfitting problem.

Noise in the training data can increase the possibility for overfitting. Noise reduction can help in reducing the overfitting.



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