Machine Learning - Practice Test

[#251] Which of the following can be true for selecting base learners for an ensemble? 1. Different learners can come from same algorithm with different hyper parameters 2. Different learners can come from different algorithms 3. Different learners can come from different training spaces

(A) 1

(B) 2

(C) 1 and 3

(D) 1, 2 and 3

(E) 1

(F) 2

(G) 1 and 3

(H) 1, 2 and 3

Correct Answer

(D) 1, 2 and 3
(H) 1, 2 and 3

[#252] In SVM, Kernel function is used to map a lower dimensional data into a higher dimensional data.

(A) TRUE

(B) FALSE

(C) TRUE

(D) FALSE

Correct Answer

(A) TRUE
(C) TRUE

[#253] You've just finished training a decision tree for spam classification, and it is getting abnormally bad performance on both your training and test sets. You know that your implementation has no bugs, so what could be causing the problem?

(A) your decision trees are too shallow.

(B) you need to increase the learning rate.

(C) you are overfitting.

(D) incorrect data

(E) your decision trees are too shallow.

(F) you need to increase the learning rate.

(G) you are overfitting.

(H) incorrect data

Correct Answer

(A) your decision trees are too shallow.
(E) your decision trees are too shallow.

[#254] Point out the wrong statement.

(A) regression through the origin yields an equivalent slope if you center the data first

(B) normalizing variables results in the slope being the correlation

(C) least squares is not an estimation tool

(D) none of the mentioned

(E) regression through the origin yields an equivalent slope if you center the data first

(F) normalizing variables results in the slope being the correlation

(G) least squares is not an estimation tool

(H) none of the mentioned

Correct Answer

(C) least squares is not an estimation tool
(G) least squares is not an estimation tool

[#255] . . . . . . . .xa0which can accept a NumPy RandomState generator or an integer seed.

(A) make_blobs

(B) random_state

(C) test_size

(D) training_size

(E) make_blobs

(F) random_state

(G) test_size

(H) training_size

Correct Answer

(B) random_state
(F) random_state

Machine Learning - Study Mode

[#251] Which of the following can be true for selecting base learners for an ensemble? 1. Different learners can come from same algorithm with different hyper parameters 2. Different learners can come from different algorithms 3. Different learners can come from different training spaces

Correct Answer

[#252] In SVM, Kernel function is used to map a lower dimensional data into a higher dimensional data.

Correct Answer

[#253] You've just finished training a decision tree for spam classification, and it is getting abnormally bad performance on both your training and test sets. You know that your implementation has no bugs, so what could be causing the problem?

Correct Answer

[#254] Point out the wrong statement.

Correct Answer

[#255] . . . . . . . .xa0which can accept a NumPy RandomState generator or an integer seed.

Correct Answer