Assessment due date is $2024-10-09,23$:$59$ IST

Last recorded submission : Assessment not submitted

$1$ point

Who proposed the original Support Vector Machine algorithm?

A$.$ Patrick Winston

B$.$ Vladimir N$.$ Vapnik

C$.$ Herbert Simon

D$.$ Tom M$.$ Mitchell

$1$ point

Intra$-$cluster cohesion measures how near the data points in a cluster are to the cluster centroid and is a reflection of $\_\_\_\_\_\_\_\_\_\_\_\_.$

A$.$ Similarity.

B$.$ Dissimilarity.

C$.$ Compactness.

D$.$ Isolation.

$1$ point

Which of the following statements are correct for a Maximum Margin Classifier? I. Best hyperplane is the one that represents the largest separation, or margin, between the two classes. II$.$ The linear classifier defined by the maximum$-$margin hyperplane is known as a maximum$-$margin classifier.

A$.$ Statements I and II

B$.$ Only Statement II

C$.$ Only Statement I

D$.$ None.

$1$ point

Exclusive reliance on dot products enables to solve non$-$linear problems in a support vector machine because of the following: I. Learning depends only on dot products of sample pairs. II$.$ Classification depends only on dot products of unknown with samples.

A$.$ Only statement I.

B$.$ Statements I and II$.$

C$.$ Only statement II$.$

D$.$ None of above.

$1$ point

The Least Square Regression is a statistical procedure to find the best fit for a set of data points. Which of the following statements express the basic idea of the best fit or the Least$-$square regression line?

A$.$ Minimizing the sum of the offsets or residuals of points from the plotted curve.

B$.$ The intercept of the least$-$squares regression line describes some specific property of the data.

C$.$ The slope of the regression line describes how much we expect the dependent variable to change, on average, for every unit change in the independent variable.

D$.$ Least$-$squares regression line is the line that makes the sum of the squares of the vertical distances of the data points from the line as small as possible.

$1$ point

Clustering is hard to evaluate, but very useful in practice. Cluster evaluation can be done using the following:

A$.$ Compactness for intra$-$cluster cohesion.

B$.$ Isolation for inter$-$cluster separation.

C$.$ Comparison with the correct clusters.

D$.$ Labelled data as ground truth for cluster evaluation through classification.

$1$ point

What is feature scaling or standardization?

A$.$ It basically helps to normalize the data within a particular range.

B$.$ It is about using transforms on variables to make their distribution more gaussian.

C$.$ It is a step of data pre$-$processing which is applied to features of data.

D$.$ It basically helps to use the least$-$squares regression equation.

$1$ point

Assertion: One of the rules of thumb for preparation of data when using ordinary Least Squares Regression is to remove collinearity. Reason: Linear regression will over$-$fit the data when we have highly correlated input variables. Mark the correct choice as

A$.$ Both A and R are true and R is the correct explanation for A

B$.$ Both A and R are true but R is not the correct explanation for A

C$.$ A is True but R is False

D$.$ A is false but R is True

$1$ point

Assertion: A cluster is a collection of data items which are $$similar$$ between them, and $$dissimilar$$ to data items in other clusters. Reason: In clustering problems, class labels are not specified; only the feature vectors representing different objects$/$instances$/$ records or situations are known. Mark the correct choice as

A$.$ Both A and R are true and R is the correct explanation for A

B$.$ Both A and R are true but R is not the correct explanation for A

C$.$ A is True but R is False

D$.$ A is false but R is True

$1$ point

$.$ Assertion: The kernel trick is an efficient way to transform data into higher dimensions. Reason: The kernel trick allows us to project data from a training set which isn't linearly separable into a higher dimensional space where it becomes linearly separable. Mark the correct choice as

A$.$ Both A and R are true and R is the correct explanation for A

B$.$ Both A and R are true but R is not the correct explanation for A

C$.$ A is True but R is False

D$.$ A is false but R is True

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