Machine Learning MCQ Questions: Practice and Test Your Knowledge

1

. . . . . . . . provides some built-in datasets that can be used for testing purposes.

A) scikit-learn B) classification C) regression D) none of the above

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Answer & Solution

Answer: Option A

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2

The leaf nodes of a model tree are

A) averages of numeric output attribute values. B) nonlinear regression equations. C) linear regression equations. D) sums of numeric output attribute values.

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Answer & Solution

Answer: Option C

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3

The parameter . . . . . . . . allows specifying the percentage of elements to put into the test/training set

A) test_size B) training_size C) All above D) None of these

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Answer & Solution

Answer: Option C

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4

How do you handle missing or corrupted data in a dataset?

A) Drop missing rows or columns B) Replace missing values with mean/median/mode C) Assign a unique category to missing values D) All of the above

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Answer & Solution

Answer: Option D

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5

What is the naive assumption in a Naive Bayes Classifier.

A) all the classes are independent of each other B) all the features of a class are independent of each other C) the most probable feature for a class is the most important feature to be cinsidered for classification D) all the features of a class are conditionally dependent on each other

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Answer & Solution

Answer: Option D

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6

Data used to optimize the parameter settings of a supervised learner model.

A) training B) test C) verification D) validation

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Answer & Solution

Answer: Option D

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7

In a real problem, you should check to see if the SVM is separable and then include slack variables if it is not separable.

A) TRUE B) FALSE

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Answer & Solution

Answer: Option B

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8

We usually use feature normalization before using the Gaussian kernel in SVM. What is true about feature normalization?
1. We do feature normalization so that new feature will dominate other
2. Some times, feature normalization is not feasible in case of categorical variables
3. Feature normalization always helps when we use Gaussian kernel in SVM

A) 1 B) 1 and 2 C) 1 and 3 D) 2 and 3

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Answer & Solution

Answer: Option B

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9

The minimum time complexity for training an SVM is O(n2). According to this fact, what sizes of datasets are not best suited for SVM's?

A) Large datasets B) Small datasets C) Medium sized datasets D) Size does not matter

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Answer & Solution

Answer: Option A

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10

This clustering algorithm initially assumes that each data instance represents a single cluster.

A) agglomerative clustering B) conceptual clustering C) k-means clustering D) expectation maximization

Discuss

Answer & Solution

Answer: Option A

Solution:

Agglomerative clustering is a type of hierarchical clustering technique.

Initially, each data instance is considered as a separate cluster.

Clusters are merged step by step based on a defined similarity measure.

This process continues until all data points form a single cluster or meet a stopping criterion.

Key Point: It is a bottom-up approach to clustering.