Machine Learning MCQ Questions with answers and solutions Page 30

Q. In reinforcement learning, this feedback is usually called as . . . . . . . .

(A) Overfitting
(B) Overlearning
(C) Reward
(D) None of above

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✅ Correct Answer: (C) Reward

Published by Mr. Dubey ⭐ 111.54K Points at 26-Apr-2024 12:23 pm in Machine Learning

Q. In multiclass classification number of classes must be

(A) less than two
(B) equals to two
(C) greater than two
(D) option 1 and option 2

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✅ Correct Answer: (C) greater than two

Published by Mr. Dubey ⭐ 111.54K Points at 26-Apr-2024 12:23 pm in Machine Learning

Q. Which of the following methods can not achieve zero training error on any linearly separable dataset?

(A) decision tree
(B) 15-nearest neighbors
(C) hard-margin svm
(D) perceptron

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✅ Correct Answer: (B) 15-nearest neighbors

Published by Mr. Dubey ⭐ 111.54K Points at 26-Apr-2024 12:23 pm in Machine Learning

Q. Imagine, you are solving a classification problems with highly imbalanced class. The majority class is observed 99% of times in the training data. Your model has 99% accuracy after taking the predictions on test data. Which of the following is true in such a case?
1. Accuracy metric is not a good idea for imbalanced class problems.
2.Accuracy metric is a good idea for imbalanced class problems.
3.Precision and recall metrics are good for imbalanced class problems.
4.Precision and recall metrics aren't good for imbalanced class problems.

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

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✅ Correct Answer: (A) 1 and 3

Published by Mr. Dubey ⭐ 111.54K Points at 26-Apr-2024 12:23 pm in Machine Learning

Q. Which of the following evaluation metrics can be used to evaluate a model while modeling a continuous output variable?

(A) AUC-ROC
(B) Accuracy
(C) Logloss
(D) Mean-Squared-Error

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✅ Correct Answer: (D) Mean-Squared-Error

Published by Mr. Dubey ⭐ 111.54K Points at 26-Apr-2024 12:23 pm in Machine Learning

Q. SVM is a algorithm

(A) classification
(B) clustering
(C) regression
(D) all

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✅ Correct Answer: (A) classification

Published by Mr. Dubey ⭐ 111.54K Points at 26-Apr-2024 12:23 pm in Machine Learning

Q. Which of the one is true about Heteroskedasticity?

(A) linear regression with varying error terms
(B) linear regression with constant error terms
(C) linear regression with zero error terms
(D) none of these

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✅ Correct Answer: (A) linear regression with varying error terms

Published by Mr. Dubey ⭐ 111.54K Points at 26-Apr-2024 12:23 pm in Machine Learning

Q. Suppose that we have N independent variables (X1, X2, Xn) and dependent variable is Y. Now Imagine that you are applying linear regression by fitting the best fit line using least square error on this data. You found that correlation coefficient for one of its variable(Say X1) with Y is -0.95. Which of the following is true for X1?

(A) relation between the x1 and y is weak
(B) relation between the x1 and y is strong
(C) relation between the x1 and y is neutral
(D) correlation can't judge the relationship

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✅ Correct Answer: (B) relation between the x1 and y is strong

Published by Mr. Dubey ⭐ 111.54K Points at 26-Apr-2024 12:23 pm in Machine Learning

Q. While using . . . . . . . . all labels are turned into sequential numbers.

(A) LabelEncoder class
(B) LabelBinarizer class
(C) DictVectorizer
(D) FeatureHasher

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✅ Correct Answer: (A) LabelEncoder class

Published by Mr. Dubey ⭐ 111.54K Points at 26-Apr-2024 12:23 pm in Machine Learning

Q. . . . . . . . . is the most drastic one and should be considered only when the dataset is quite large, the number of missing features is high, and any prediction could be risky.

(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

💬 Discuss

✅ Correct Answer: (A) Removing the whole line

Published by Mr. Dubey ⭐ 111.54K Points at 26-Apr-2024 12:23 pm in Machine Learning

Machine Learning MCQs with answers Page - 30

Q. In reinforcement learning, this feedback is usually called as . . . . . . . .

Q. In multiclass classification number of classes must be

Q. Which of the following methods can not achieve zero training error on any linearly separable dataset?

Q. Which of the following evaluation metrics can be used to evaluate a model while modeling a continuous output variable?

Q. SVM is a algorithm

Q. Which of the one is true about Heteroskedasticity?

Q. While using . . . . . . . . all labels are turned into sequential numbers.

Q. . . . . . . . . is the most drastic one and should be considered only when the dataset is quite large, the number of missing features is high, and any prediction could be risky.

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