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

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__Machine
learning MCQ - Set 06__

__Machine learning MCQ - Set 06__

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*1. Which of the
following guidelines is applicable to initialization of the weight vector in a
fully connected neural network.*

a) Should not set
it to zero since otherwise it will cause overfitting

b) Should not set it to zero since otherwise (stochastic)
gradient descent will explore a very small space

c) Should set it to
zero since otherwise it causes a bias

d) Should set it to
zero in order to preserve symmetry across all neurons

**View Answer**Answer: (b) should not set it to zero since otherwise gradient
descent will explore a very small spaceIf we initialize
all the weights to zero, the neural network will train but all the neurons
will learn the same features during training. Setting all weights to zero
makes your model equivalent to a linear model. When you set all weight to 0,
the derivative with respect to loss function is the same for every w in weight
matrix, thus, all the weights have the same values in the subsequent
iteration. Hence, they must be initialized to random numbers. |

**2. Given two Boolean random variables, A and B, where P(A) = ½, P(B) = 1/3, and P(A | ¬B) = ¼, what is P(A | B)?**

a) 1/6

b) ¼

c) ¾

d) 1

**View Answer**Answer: (d) 1P(A | B) = (P(B |
A) P(A)) / P(B) = 3/2 P(B | A). P(B | A) = 1 –
P(~B | A), and P(~B | A) = (P(A | ~B) P(~B)) / P(A) = 1/3, so P(B | A) = 2/3
and therefore P(A | B) =
(2/3)(3/2) = 1 |

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*3. For a neural
network, which one of these structural assumptions is the one that most affects
the trade-off between underfitting (i.e. a high bias model) and overfitting
(i.e. a high variance model):*

a) The number of hidden nodes

b) The learning
rate

c) The initial
choice of weights

d) The use of a
constant-term unit input

**View Answer**Answer: (a) The number of hidden nodesThe number of
hidden nodes. 0 will result in a linear model, which many (with non-linear
activation) significantly increases the variance of the model. A feed forward
neural network without hidden nodes can only find linear decision boundaries.
The Hidden nodes
perform computations and transfer information from the input nodes to the
output nodes. A collection of hidden nodes forms a “Hidden Layer”. While
a feedforward network will only have a single input layer and a
single output layer, it can have zero or multiple Hidden Layers.##
Overfitting: If
there are so many neurons in the hidden layers it might cause Overfitting.
Overfitting occurs when unnecessary more neurons are present in the network.##
Underfitting: If
the number of neurons are less as compared to the complexity of the problem
data it takes towards the Underfitting. It occurs when there are few neurons
in the hidden layers to detect the signal in complicated data set. |

**4. You've just finished training a decision tree for spam classification, and it is getting abnormally bad performance on both your training and test sets. You know that your implementation has no bugs, so what could be causing the problem?**

a) Your decision trees are too shallow.

b) You need to
increase the learning rate.

c) You are overfitting.

d) None of the
above.

**View Answer**Answer: (a) your decision trees are too shallowShallow decision
trees - trees that are too shallow might lead to overly simple models that
can’t fit the data.A model that is underfit will have high training and
high testing error. Hence, bad performance on training and test sets
indicates underfitting which means the set of hypotheses are not complex
enough (decision trees that are shallow ) to include the true but unknown
prediction function.##
The shallower the
tree the less variance we have in our predictions; however, at some point we
can start to inject too much bias as shallow trees (e.g., stumps) are not
able to capture interactions and complex patterns in our data. |

**5. ___________ refers to a model that can neither model the training data nor generalize to new data.**

a) good fitting

b) overfitting

c) underfitting

d) all of the above

**View Answer**Answer: (c) underfittingAn underfit
machine learning model is not a suitable model and will be obvious as it will
have poor performance on the training data. Usually, a model that is underfit will have high training and high testing error |

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