Machine Learning - Practice Test

[#51] Suppose you are training a linear regression model. Now consider these points. 1. Overfitting is more likely if we have less data 2. Overfitting is more likely when the hypothesis space is small.Which of the above statement(s) are correct?

(A) both are false

(B) 1 is false and 2 is true

(C) 1 is true and 2 is false

(D) both are true

(E) both are false

(F) 1 is false and 2 is true

(G) 1 is true and 2 is false

(H) both are true

Correct Answer

(C) 1 is true and 2 is false
(G) 1 is true and 2 is false

[#52] Gini index does not favour equal sized partitions.

(A) TRUE

(B) FALSE

(C) TRUE

(D) FALSE

Correct Answer

(B) FALSE
(D) FALSE

[#53] what is the function of 'Unsupervised Learning'?

(A) Find clusters of the data and find low-dimensional representations of the data

(B) Find interesting directions in data and find novel observations/ database cleaning

(C) Interesting coordinates and correlations

(D) All

(E) Find clusters of the data and find low-dimensional representations of the data

(F) Find interesting directions in data and find novel observations/ database cleaning

(G) Interesting coordinates and correlations

(H) All

Correct Answer

(D) All
(H) All

[#54] In an election, N candidates are competing against each other and people are voting for either of the candidates. Voters don't communicate with each other while casting their votes. Which of the following ensemble method works similar to above-discussed election procedure? Hint: Persons are like base models of ensemble method.

(A) bagging

(B) boosting

(C) a or b

(D) none of these

(E) bagging

(F) boosting

(G) a or b

(H) none of these

Correct Answer

(A) bagging
(E) bagging

[#55] The soft margin SVM is more preferred than the hard-margin SVM when-

(A) the data is linearly seperable

(B) the data is noisy and contains overlapping points

(C) the data is not noisy and linearly seperable

(D) the data is noisy and linearly seperable

(E) the data is linearly seperable

(F) the data is noisy and contains overlapping points

(G) the data is not noisy and linearly seperable

(H) the data is noisy and linearly seperable

Correct Answer

(B) the data is noisy and contains overlapping points
(F) the data is noisy and contains overlapping points

Machine Learning - Study Mode

[#51] Suppose you are training a linear regression model. Now consider these points. 1. Overfitting is more likely if we have less data 2. Overfitting is more likely when the hypothesis space is small.Which of the above statement(s) are correct?

Correct Answer

[#52] Gini index does not favour equal sized partitions.

Correct Answer

[#53] what is the function of 'Unsupervised Learning'?

Correct Answer

Correct Answer

[#55] The soft margin SVM is more preferred than the hard-margin SVM when-

Correct Answer