Machine Learning MCQ Questions with answers and solutions Page 23

Q. SVM is a learning

(A) supervised
(B) unsupervised
(C) both
(D) none

💬 Discuss

✅ Correct Answer: (A) supervised

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

Q. Which of the following quantities are minimized directly or indirectly during parameter estimation in Gaussian distribution Model?

(A) negative log-likelihood
(B) log-liklihood
(C) cross entropy
(D) residual sum of square

💬 Discuss

✅ Correct Answer: (A) negative log-likelihood

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

Q. What does K refers in the K-Means algorithm which is a non-hierarchical clustering approach?

(A) complexity
(B) fixed value
(C) no of iterations
(D) number of clusters

💬 Discuss

✅ Correct Answer: (D) number of clusters

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

Q. If {A,B,C,D} is a frequent itemset, candidate rules which is not possible is

(A) c → a
(B) d → abcd
(C) a → bc
(D) b → adc

💬 Discuss

✅ Correct Answer: (B) d → abcd

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

Q. Below are the two ensemble models: 1. E1(M1, M2, M3) and 2. E2(M4, M5, M6) Above, Mx is the individual base models. Which of the following are more likely to choose if following conditions for E1 and E2 are given? E1: Individual Models accuracies are high but models are of the same type or in another term less diverse E2: Individual Models accuracies are high but they are of different types in another term high diverse in nature

(A) e1
(B) e2
(C) any of e1 and e2
(D) none of these

💬 Discuss

✅ Correct Answer: (B) e2

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

Q. Naive Bayes classifiers are a collection . . . . . . . . of algorithms

(A) Classification
(B) Clustering
(C) Regression
(D) All

💬 Discuss

✅ Correct Answer: (A) Classification

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

Q. The K-means algorithm:

(A) requires the dimension of the feature space to be no bigger than the number of samples
(B) has the smallest value of the objective function when k = 1
(C) minimizes the within class variance for a given number of clusters
(D) converges to the global optimum if and only if the initial means are chosen as some of the samples themselves

💬 Discuss

✅ Correct Answer: (C) minimizes the within class variance for a given number of clusters

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

Q. What is true about an ensembled classifier?
1. Classifiers that are more "sure" can vote with more conviction
2. Classifiers can be more "sure" about a particular part of the space
3. Most of the times, it performs better than a single classifier

(A) 1 and 2
(B) 1 and 3
(C) 2 and 3
(D) all of the above

💬 Discuss

✅ Correct Answer: (D) all of the above

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

Q. 100 people are at party. Given data gives information about how many wear pink or not, and if a man or not. Imagine a pink wearing guest leaves, what is the probability of being a man

(A) 0.4
(B) 0.2
(C) 0.6
(D) 0.45

💬 Discuss

✅ Correct Answer: (B) 0.2

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

Q. How to select best hyperparameters in tree based models?

(A) measure performance over training data
(B) measure performance over validation data
(C) both of these
(D) random selection of hyper parameters

💬 Discuss

✅ Correct Answer: (B) measure performance over validation data

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

Machine Learning MCQs with answers Page - 23

Q. SVM is a learning

Q. Which of the following quantities are minimized directly or indirectly during parameter estimation in Gaussian distribution Model?

Q. What does K refers in the K-Means algorithm which is a non-hierarchical clustering approach?

Q. If {A,B,C,D} is a frequent itemset, candidate rules which is not possible is

Q. Naive Bayes classifiers are a collection . . . . . . . . of algorithms

Q. The K-means algorithm:

Q. What is true about an ensembled classifier?
1. Classifiers that are more "sure" can vote with more conviction
2. Classifiers can be more "sure" about a particular part of the space
3. Most of the times, it performs better than a single classifier

Q. 100 people are at party. Given data gives information about how many wear pink or not, and if a man or not. Imagine a pink wearing guest leaves, what is the probability of being a man

Q. How to select best hyperparameters in tree based models?

Programming Categories

📱 Download Our App

Machine Learning MCQs with answers Page - 23

Q. SVM is a learning

Q. Which of the following quantities are minimized directly or indirectly during parameter estimation in Gaussian distribution Model?

Q. What does K refers in the K-Means algorithm which is a non-hierarchical clustering approach?

Q. If {A,B,C,D} is a frequent itemset, candidate rules which is not possible is

Q. Naive Bayes classifiers are a collection . . . . . . . . of algorithms

Q. The K-means algorithm:

Q. What is true about an ensembled classifier? 1. Classifiers that are more "sure" can vote with more conviction 2. Classifiers can be more "sure" about a particular part of the space 3. Most of the times, it performs better than a single classifier

Q. 100 people are at party. Given data gives information about how many wear pink or not, and if a man or not. Imagine a pink wearing guest leaves, what is the probability of being a man

Q. How to select best hyperparameters in tree based models?

Programming Categories

📱 Download Our App

Q. What is true about an ensembled classifier?
1. Classifiers that are more "sure" can vote with more conviction
2. Classifiers can be more "sure" about a particular part of the space
3. Most of the times, it performs better than a single classifier