Advanced Database Management System - Tutorials and Notes: Machine Learning Exam Questions TRUE or FALSE 10

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Monday, 18 May 2020

Machine Learning Exam Questions TRUE or FALSE 10

Machine learning quiz questions TRUE or FALSE with answers, important machine learning interview questions for data science, Top 5 machine learning question set

Machine Learning TRUE / FALSE Questions - SET 10

1. A classifier trained on less training data is less likely to overfit.

(a) TRUE                                                   (b) FALSE

View Answer

Answer: FALSE
A specific classifier (with some fixed model complexity) will be more likely to fit to the noise in the training data when there is less training data, and is therefore more likely to overfit.

2. Logistic regression cannot be kernelized.

(a) TRUE                                                   (b) FALSE

View Answer

Answer: FALSE
Logistic regression can be kernelized.
Regular logistic regression works well for linearly separable data. It’s weakness is with non-linearly separable data. Kernel logistic regression is a technique that extends regular logistic regression to deal with data that is not linearly separable.

3. The following product of factors (joint probability computation) corresponds to a valid Bayesian Network over the variables A, B, C and D: P(A | B) * P(B | C) * P(C | D) * P(D | A).
(a) TRUE                                                   (b) FALSE

View Answer

Answer: FALSE

The Bayesian network as per the given specification is as follows, if you draw a Bayesian network;

Bayesian network

The network generated from the given conditional probabilities results in a cyclic graph. For a valid Bayesian network, it should be a Directed Acyclic Graph (DAG). Hence, the given specification does not correspond to a valid Bayesian network.
Bayesian network
A Bayesian network is a directed acyclic graph in which each edge corresponds to a conditional dependency, and each node corresponds to a unique random variable.


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