Advanced Database Management System - Tutorials and Notes: Machine Learning Multiple Choice Questions with Answers 28
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Sunday, 6 June 2021

Machine Learning Multiple Choice Questions with Answers 28

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, question bank in machine learning, subset selection, overfitting, SVM classifier, slack variables, generative models, generative models can be used for classification

Machine learning Quiz Questions - Set 28


1. Which of the following is true about generative models?

a) They capture the joint probability

b) The perceptron is a generative model

c) Generative models can be used for classification

d) They capture the conditional probability

Answer: (a) They capture the joint probability and (c) Generative models can be used for classification

Generative models are useful for unsupervised learning tasks. A generative model learns parameters by maximizing the joint probability P(X,Y). Generative models encode full probability distributions and specify how to generate data that fit such distributions. Bayesian networks are well-known examples of such models. Refer here for more information.


Generative Classifiers tries to model class, i.e., what are the features of the class. In short, it models how a particular class would generate input data. When a new observation is given to these classifiers, it tries to predict which class would have most likely generated the given observation. Refer here for more information. 


2. Which of the following are true about subset selection?

a) Subset selection can substantially decrease the bias of support vector machines

b) Ridge regression frequently eliminates some of the features

c) Finding the true best subset takes exponential time

d) Subset selection can reduce overfitting

Answer: (d) Subset selection can reduce overfitting

A classifier is said to overfit to a dataset if it models the training data too closely and gives poor predictions on new data. This occurs when there is insufficient data to train the classifier and the data does not fully cover the concept being learned.

Subset selection reduces over-fitting.

Feature subset selection is the process of identifying and removing as much of the irrelevant and redundant information as possible. This reduces the dimensionality of the data and allows learning algorithms to operate faster and more effectively.


3. What can help to reduce overfitting in an SVM classifier?

a) High-degree polynomial features

b) Setting a very low learning rate

c) Use of slack variables

d) Normalizing the data

Answer: (c) Use of slack variables

The reason that SVMs tend to be resistant to over-fitting, even in cases where the number of attributes is greater than the number of observations, is that it uses regularization. The key to avoid over-fitting lies in careful tuning of the regularization parameter, C, and in the case of non-linear SVMs, careful choice of kernel and tuning of the kernel parameters.

Without slack variables the SVM would be forced into always fitting the data exactly and would often overfit as a result.



Related links:

List the type of regularized regression

Multiple choice quiz questions in machine learning

What is the use of slack variables in SVM

over-fitting in SVM classifier

How generative models used for classification

subset selection can reduce overfitting in SVM

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