Machine Learning - Practice Test

[#191] PCA is

(A) forward feature selection

(B) backword feature selection

(C) feature extraction

(D) all of the above

(E) forward feature selection

(F) backword feature selection

(G) feature extraction

(H) all of the above

Correct Answer

(C) feature extraction
(G) feature extraction

[#192] Which statement is true about prediction problems?

(A) the output attribute must be categorical.

(B) the output attribute must be numeric.

(C) the resultant model is designed to determine future outcomes.

(D) the resultant model is designed to classify current behavior.

(E) the output attribute must be categorical.

(F) the output attribute must be numeric.

(G) the resultant model is designed to determine future outcomes.

(H) the resultant model is designed to classify current behavior.

Correct Answer

(D) the resultant model is designed to classify current behavior.
(H) the resultant model is designed to classify current behavior.

[#193] . . . . . . . . is much more difficult because it's necessary to determine a supervised strategy to train a model for each feature and, finally, to predict their value

(A) removing the whole line

(B) creating sub-model to predict those features

(C) using an automatic strategy to input them according to the other known values

(D) all above

(E) removing the whole line

(F) creating sub-model to predict those features

(G) using an automatic strategy to input them according to the other known values

(H) all above

Correct Answer

(B) creating sub-model to predict those features
(F) creating sub-model to predict those features

[#194] Which of the folllowing is an example of feature extraction?

(A) construction bag of words from an email

(B) applying pca to project high dimensional data

(C) removing stop words

(D) forward selection

(E) construction bag of words from an email

(F) applying pca to project high dimensional data

(G) removing stop words

(H) forward selection

Correct Answer

(B) applying pca to project high dimensional data
(F) applying pca to project high dimensional data

[#195] Clustering is . . . . . . . . and is example of . . . . . . . . learning

(A) predictive and supervised

(B) predictive and unsupervised

(C) descriptive and supervised

(D) descriptive and unsupervised

(E) predictive and supervised

(F) predictive and unsupervised

(G) descriptive and supervised

(H) descriptive and unsupervised

Correct Answer

(D) descriptive and unsupervised
(H) descriptive and unsupervised

Machine Learning - Study Mode

[#191] PCA is

Correct Answer

[#192] Which statement is true about prediction problems?

Correct Answer

[#193] . . . . . . . . is much more difficult because it's necessary to determine a supervised strategy to train a model for each feature and, finally, to predict their value

Correct Answer

[#194] Which of the folllowing is an example of feature extraction?

Correct Answer

[#195] Clustering is . . . . . . . . and is example of . . . . . . . . learning

Correct Answer