#
*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.*

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

(a)
TRUE (b) FALSE

**View Answer**Answer: FALSEA 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.*

*2. Logistic regression cannot be kernelized.*

(a)
TRUE (b) FALSE

**View Answer**Answer: FALSELogistic
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;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 networkA 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. |

**************************

###
**Related links:**

**Related links:**

## No comments:

## Post a comment