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Mr. Dubey • 51.17K Points
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Q. 31) What are support vectors?

(A) all the examples that have a non-zero weight ??k in a svm

(B) the only examples necessary to compute f(x) in an svm.

(C) all of the above

(D) none of the above

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Mr. Dubey • 51.17K Points
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Q. 32) A perceptron adds up all the weighted inputs it receives, and if it exceeds a certain value, it outputs a 1, otherwise it just outputs a 0.

(A) true

(B) false

(C) sometimes – it can also output intermediate values as well

(D) can’t say

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Mr. Dubey • 51.17K Points
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Q. 33) What is the purpose of the Kernel Trick?

(A) to transform the data from nonlinearly separable to linearly separable

(B) to transform the problem from regression to classification

(C) to transform the problem from supervised to unsupervised learning.

(D) all of the above

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Mr. Dubey • 51.17K Points
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Q. 34) Which of the following can only be used when training data are linearlyseparable?

(A) linear hard-margin svm

(B) linear logistic regression

(C) linear soft margin svm

(D) parzen windows

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Mr. Dubey • 51.17K Points
Coach

Q. 35) The firing rate of a neuron

(A) determines how strongly the dendrites of the neuron stimulate axons of neighboring neurons

(B) is more analogous to the output of a unit in a neural net than the output voltage of the neuron

(C) only changes very slowly, taking a period of several seconds to make large adjustments

(D) can sometimes exceed 30,000 action potentials per second

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Mr. Dubey • 51.17K Points
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Q. 36) Which of the following evaluation metrics can not be applied in case of logistic regression output to compare with target?

(A) auc-roc

(B) accuracy

(C) logloss

(D) mean-squared-error

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Mr. Dubey • 51.17K Points
Coach

Q. 37) The cost parameter in the SVM means:

(A) the number of cross-validations to be made

(B) the kernel to be used

(C) the tradeoff between misclassification and simplicity of the model

(D) none of the above

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Mr. Dubey • 51.17K Points
Coach

Q. 38) The kernel trick

(A) can be applied to every classification algorithm

(B) is commonly used for dimensionality reduction

(C) changes ridge regression so we solve a d ?? d linear system instead of an n ?? n system, given n sample points with d features

(D) exploits the fact that in many learning algorithms, the weights can be written as a linear combination of input points

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Mr. Dubey • 51.17K Points
Coach

Q. 39) How does the bias-variance decomposition of a ridge regression estimator compare with that of ordinary least squares regression?

(A) ridge has larger bias, larger variance

(B) ridge has smaller bias, larger variance

(C) ridge has larger bias, smaller variance

(D) ridge has smaller bias, smaller variance

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Mr. Dubey • 51.17K Points
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Q. 40) Which of the following are real world applications of the SVM?

(A) text and hypertext categorization

(B) image classification

(C) clustering of news articles

(D) all of the above

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