#
*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 08__

__Machine learning MCQ - Set 08__

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**1. Which among the
following prevents overfitting when we perform bagging?**

**1. Which among the following prevents overfitting when we perform bagging?**

a) The use of
sampling with replacement as the sampling technique

b) The use of weak classifiers

c) The use of
classification algorithms which are not prone to overfitting

d) The practice of
validation performed on every classifier trained

**View Answer**Answer: (b) the use of weak classifiersThe presence of
over-training (which leads to overfitting) is not generally a problem with weak
classifiers. For example, in decision stumps, i.e., decision trees with only
one node (the root node), there is no real scope for overfitting. This helps
the classifier which combines the outputs of weak classifiers in avoiding
overfitting.. |

**2. Averaging the output of multiple decision trees helps ________.**
a) Increase bias

b) Decrease bias

c) Increase
variance

d) Decrease variance

**View Answer**Answer: (d) decrease variance ##
Averaging out the
predictions of multiple classifiers will drastically reduce the variance.Averaging is not
specific to decision trees; it can work with many different learning algorithms.
But it works particularly well with decision trees.##
Why averaging?If two trees pick
different features for the very first split at the top of the tree, then it’s
quite common for the trees to be completely different. So decision trees tend
to have high variance. To fix this, we can reduce the variance of decision
trees by taking an average answer of a bunch of decision trees. |

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*3. If N is the
number of instances in the training dataset, nearest neighbors has a
classification run time of *

a) O(1)

b) O( N )

c) O(log N )

d) O( N 2 )

**View Answer**Answer: (b) O(N) Nearest neighbors
needs to compute distances to each of the N training instances. Hence, the
classification run time complexity is O(N). |

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*4. Which among the
following is/are some of the assumptions made by the k-means algorithm (assuming
Euclidean distance measure)?*

a) Clusters are spherical in shape

b) Clusters are of similar sizes

c) Data points in
one cluster are well separated from data points of other clusters

d) There is no wide
variation in density among the data points

**View Answer**Answer: (a) and (b) clusters are spherical in shape and of
similar sizes The Euclidean
distance measure ensures that areas around a cluster centroid comprising
points closest to that centroid (which is a cluster) is spherical in shape.
Also, this particular distance measure prevents arbitrarily sized clusters
since this typically violates the clustering criterion. |

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*5. Which of the
following is more appropriate to do feature selection?*

a) Ridge

b) Lasso

c) both (a) and (b)

d) neither (a) nor
(b)

**View Answer**Answer: (b) lasso For feature
selection, we would prefer to use lasso since solving the optimization
problem when using lasso will cause some of the coefficients to be exactly
zero (depending of course on the data) whereas with ridge regression, the
magnitude of the coefficients will be reduced, but won't go down to zero.##
Ridge and Lasso##
Ridge and Lasso are types of regularization
techniques. They are the simple techniques to reduce model complexity and
prevent over-fitting which may result from simple linear regression. |

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