#
*Machine
learning quiz questions TRUE or FALSE with answers, important machine
learning interview questions for data science, Top 3 machine learning
question set*

##
__Machine Learning TRUE / FALSE Questions - SET 06__

###
*1. The
back-propagation algorithm, when run until a minimum is achieved, always finds
the same solution (i.e., weights) no matter what the initial set of weights
are.*

*1. The back-propagation algorithm, when run until a minimum is achieved, always finds the same solution (i.e., weights) no matter what the initial set of weights are.*

(a)
TRUE (b)
FALSE

**View Answer**Answer: FALSEIt will iterate
until a local minimum in the squared error is reached.##
Back-propagation
algorithmThe algorithm is used to train a neural network
through a method called chain rule. In simple terms, after each forward pass
through a network, back-propagation performs a backward pass while adjusting
the model’s parameters (weights and biases). |

###
*2. Since classification is a special case of regression,
logistic regression is a special case of linear regression.*

*2. Since classification is a special case of regression, logistic regression is a special case of linear regression.*

(a)
TRUE (b)
FALSE

**View Answer**Answer: TRUELogistic
regression is a special case of linear regression when the outcome variable
is categorical, and the log of odds is the dependent variable. It predicts
the probability of occurrence of an event by fitting data to a logit
function. Thus, the logit function acts as a link between logistic regression
and linear regression. |

###
*3. K-Means will
always give the same results regardless of the initialization of the centroids.*

*3. K-Means will always give the same results regardless of the initialization of the centroids.*

(a)
TRUE (b)
FALSE

**View Answer**Answer: FALSE##
K-means is sensitive
to different initializations, which is why you should run it multiple times
from different random initializations.One way to
determine the number of clusters is the elbow method. |

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

###
**Related links:**

**Related links:**