Machine Learning MCQ Questions with answers and solutions Page 27

Q. The standard error is defined as the square root of this computation.

(A) the sample variance divided by the total number of sample instances.
(B) the population variance divided by the total number of sample instances.
(C) the sample variance divided by the sample mean.
(D) the population variance divided by the sample mean.

💬 Discuss

✅ Correct Answer: (A) the sample variance divided by the total number of sample instances.

Published by Mr. Dubey ⭐ 111.54K Points at 26-Apr-2024 12:22 pm in Machine Learning

Q. Bernoulli Nave Bayes Classifier is . . . . . . . .distribution

(A) Continuous
(B) Discrete
(C) Binary
(D) none of these

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✅ Correct Answer: (C) Binary

Published by Mr. Dubey ⭐ 111.54K Points at 26-Apr-2024 12:22 pm in Machine Learning

Q. How many terms are required for building a bayes model?

(A) 1
(B) 2
(C) 3
(D) 4

💬 Discuss

✅ Correct Answer: (C) 3

Published by Mr. Dubey ⭐ 111.54K Points at 26-Apr-2024 12:22 pm in Machine Learning

Q. Suppose you are building a SVM model on data X. The data X can be error prone which means that you should not trust any specific data point too much. Now think that you want to build a SVM model which has quadratic kernel function of polynomial degree 2 that uses Slack variable C as one of it's hyper parameter.What would happen when you use very small C (C~0)?

(A) Misclassification would happen
(B) Data will be correctly classified
(C) Can't say
(D) None of these

💬 Discuss

✅ Correct Answer: (A) Misclassification would happen

Published by Mr. Dubey ⭐ 111.54K Points at 26-Apr-2024 12:22 pm in Machine Learning

Q. Which of the following is a reasonable way to select the number of principal components "k"?

(A) choose k to be the smallest value so that at least 99% of the varinace is retained. - answer
(B) choose k to be 99% of m (k = 0.99*m, rounded to the nearest integer).
(C) choose k to be the largest value so that 99% of the variance is retained.
(D) use the elbow method

💬 Discuss

✅ Correct Answer: (A) choose k to be the smallest value so that at least 99% of the varinace is retained. - answer

Published by Mr. Dubey ⭐ 111.54K Points at 26-Apr-2024 12:22 pm in Machine Learning

Q. If you need a more powerful scaling feature, with a superior control on outliers and the possibility to select a quantile range, there's also the class . . . . . . . .

(A) RobustScaler
(B) DictVectorizer
(C) LabelBinarizer
(D) FeatureHasher

💬 Discuss

✅ Correct Answer: (A) RobustScaler

Published by Mr. Dubey ⭐ 111.54K Points at 26-Apr-2024 12:22 pm in Machine Learning

Q. Linear SVMs have no hyperparameters that need to be set by cross-validation

(A) TRUE
(B) underfitting
(C) ---
(D) ---

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✅ Correct Answer: (B) underfitting

Published by Mr. Dubey ⭐ 111.54K Points at 26-Apr-2024 12:22 pm in Machine Learning

Q. Impact of high variance on the training set ?

(A) overfitting
(B) vaiance
(C) both underfitting & overfitting
(D) depents upon the dataset

💬 Discuss

✅ Correct Answer: (A) overfitting

Published by Mr. Dubey ⭐ 111.54K Points at 26-Apr-2024 12:22 pm in Machine Learning

Q. Which of the following are components of generalization Error?

(A) bias
(B) the output of both models is a categorical attribute value
(C) both of them
(D) none of them

💬 Discuss

✅ Correct Answer: (C) both of them

Published by Mr. Dubey ⭐ 111.54K Points at 26-Apr-2024 12:22 pm in Machine Learning

Q. Which statement is true about neural network and linear regression models?

(A) both techniques build models whose output is determined by a linear sum of weighted input attribute values
(B) the only examples necessary to compute f(x) in an svm.
(C) both models require numeric attributes to range between 0 and 1
(D) both models require input attributes to be numeric

💬 Discuss

✅ Correct Answer: (D) both models require input attributes to be numeric

Published by Mr. Dubey ⭐ 111.54K Points at 26-Apr-2024 12:22 pm in Machine Learning

Machine Learning MCQs with answers Page - 27

Q. The standard error is defined as the square root of this computation.

Q. Bernoulli Nave Bayes Classifier is . . . . . . . .distribution

Q. How many terms are required for building a bayes model?

Q. Which of the following is a reasonable way to select the number of principal components "k"?

Q. If you need a more powerful scaling feature, with a superior control on outliers and the possibility to select a quantile range, there's also the class . . . . . . . .

Q. Linear SVMs have no hyperparameters that need to be set by cross-validation

Q. Impact of high variance on the training set ?

Q. Which of the following are components of generalization Error?

Q. Which statement is true about neural network and linear regression models?

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