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[#246] Which among the following statements best describes our approach to learning decision trees

(A) identify the best partition of the input space and response per partition to minimise sum of squares error

(B) identify the best approximation of the above by the greedy approach (to identifying the partitions)

(C) identify the model which gives the best performance using the greedy approximation (option (b)) with the smallest partition scheme

(D) identify the model which gives performance close to the best greedy approximation performance (option (b)) with the smallest partition scheme

(E) identify the best partition of the input space and response per partition to minimise sum of squares error

(F) identify the best approximation of the above by the greedy approach (to identifying the partitions)

(G) identify the model which gives the best performance using the greedy approximation (option (b)) with the smallest partition scheme

(H) identify the model which gives performance close to the best greedy approximation performance (option (b)) with the smallest partition scheme

Correct Answer

(B) identify the best approximation of the above by the greedy approach (to identifying the partitions)
(F) identify the best approximation of the above by the greedy approach (to identifying the partitions)

[#247] Which of the following techniques would perform better for reducing dimensions of a data set?

(A) removing columns which have too many missing values

(B) removing columns which have high variance in data

(C) removing columns with dissimilar data trends

(D) none of these

(E) removing columns which have too many missing values

(F) removing columns which have high variance in data

(G) removing columns with dissimilar data trends

(H) none of these

Correct Answer

(A) removing columns which have too many missing values
(E) removing columns which have too many missing values

[#248] . . . . . . . . can be adopted when it's necessary to categorize a large amount of data with a few complete examples or when there's the need to impose some constraints to a clustering algorithm.

(A) Supervised

(B) Semi-supervised

(C) Reinforcement

(D) Clusters

(E) Supervised

(F) Semi-supervised

(G) Reinforcement

(H) Clusters

Correct Answer

(B) Semi-supervised
(F) Semi-supervised

[#249] Binarize parameter in BernoulliNB scikit sets threshold for binarizing of sample features.

(A) TRUE

(B) FALSE

(C) TRUE

(D) FALSE

Correct Answer

(A) TRUE
(C) TRUE

[#250] Suppose we train a hard-margin linear SVM on n > 100 data points in R2, yielding a hyperplane with exactly 2 support vectors. If we add one more data point and retrain the classifier, what is the maximum possible number of support vectors for the new hyperplane (assuming the n + 1 points are linearly separable)?

(A) 2

(B) 3

(C) n

(D) n+1

(E) 2

(F) 3

(G) n

(H) n+1

Correct Answer

(D) n+1
(H) n+1