#
*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*

##
__Machine
learning MCQ - Set 10__

__Machine learning MCQ - Set 10__

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**1. The numerical
output of a sigmoid node in a neural network:**

**1. The numerical output of a sigmoid node in a neural network:**

a) Is unbounded,
encompassing all real numbers.

b) Is unbounded,
encompassing all integers.

c) Is bounded between 0 and 1.

d) Is bounded
between -1 and 1.

**View Answer**Answer: (d) all of the aboveThe function that
determines the output of a neuron is known as the activation function. The activation function is a mathematical
“gate” in between the input feeding the current neuron and its output going
to the next layer.The Sigmoid
function takes a value as input and outputs another value between 0 and 1. It
is non-linear and easy to work with when constructing a neural network model.
The good part about this function is that continuously differentiable over
different values of z and has
a fixed output range.##
Unlike linear
function, the output of the sigmoid activation function is always going to be
in range (0,1) |

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**2. What would
you do in PCA to get the same projection as SVD?**

**2. What would you do in PCA to get the same projection as SVD?**

a) Transform data to zero mean

b) Transform data
to zero median

c) Not possible

d) None of these

**View Answer**Answer: (a) transform data to zero meanWhen the data has
a zero mean vector PCA will have same projections as SVD, otherwise you have
to centre the data first before taking SVD. |

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*3. Regarding bias
and variance, which of the following statements are true? (Here ‘high’ and
‘low’ are relative to the ideal model.)*

a) Models which
overfit have a high bias.

b) Models which overfit have a low bias.

c) Models which
underfit have a high variance.

d) Models which underfit have a low variance

**View Answer**Answer: (b) and (d) models which overfit have a low bias and
models which underfit have a low variance##
Overfitting: Good performance on the training data,
poor generliazation to other data.In supervised
learning, overfitting happens
when the model captures the noise along with the underlying pattern in data.
These models have low bias and high variance##
Underfitting:
Poor performance on the training data and poor generalization to other dataIn supervised
learning, underfitting happens
when a model is unable to grasp the basis of data pattern. These models
usually have high bias and low variance. |

**4. Given a large dataset of medical records from patients suffering from heart disease, try to learn whether there might be different clusters of such patients for which we might tailor separate treatments. What kind of learning problem is this?**

a) Supervised
learning

b) Unsupervised learning

c) Both (a) and (b)

d) Neither (a) nor
(b)

**View Answer**Answer: (b) Unsupervised learningThis is an
unsupervised learning problem. In unsupervised learning we feed only the
input and let the algorithm to detect the output. Clustering algorithm can be
used to solve this problem by grouping patients into different clusters. |

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*5. Predicting on
whether will it rain or not tomorrow evening at a particular time is a type of
_________ problem.*

a) Classification

b) Regression

c) Unsupervised
learning

d) All of the above

**View Answer**Answer: (c) Classification problemThe result
expected here is either yes or no. That means, it can fall into one of these
classes. Classification is appropriate when we are trying to predict one of a
small number of discrete-valued outputs, such as whether it will rain (which
we might designate as class 0), or not (say class 1). |

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**Related links:**