Thursday, October 22, 2020

Machine Learning Multiple Choice Questions and Answers 20

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, question bank in machine learning, k-means, elbow method, decision tree, entropy calculation

Machine learning MCQ - Set 20

1. Which of the following clustering algorithm requires the number of clusters to be pre-specified?

a) hierarchical clustering

b) k-means clustering


d) Markov clustering algorithm

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2. Identify the best method that is used for finding optimal clusters in k-means algorithm.

a) Euclidean method

b) Manhattan method

c) Elbow method

d) Silhouette method

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3. We are dealing with samples x where x is a single value. We would like to test two alternative regression models:

1) y = ax + e

2) y = ax + bx2 + e

Which of these regression models is more appropriate to fit the training data better?

a) model 1

b) model 2

c) both will equally fit

d) not enough data

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4. If we would like to produce learning rules that are easily interpreted by humans, which of the following machine learning task would we use?

a) Logistic regression

b) Nearest neighbor

c) Decision tree learning

d) Support Vector Machine

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5. Following are the target values predicted by a decision tree in a training dataset which we used to find whether a person have passed in interview or not.

[T, T, T, F, F, T, T, T]

What is the entropy H(pass)?

a) –(2/8 log22/8 + 6/8 log26/8)

b) –(2/8 log22/8 + 4/8 log24/8)

c) –(2/6 log22/6 + 6/2 log26/2)

d) 2/8 log22/8 + 6/8 log26/8

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Related links:

How to calculate the entropy of a target variable

Which of the machine learning method would produce rules that are easily interpreted by humans

Which regression model best fit the training data better

Why the regression model with more parameter better fit the training data

Optimal cluster finding method in k-means

Why do we need to specify the number of clusters beforehand in k-means clustering

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