Machine Learning MCQ Questions with answers and solutions Page 25

Q. The parameter . . . . . . . . allows specifying the percentage of elements to put into the test/training set

(A) test_size
(B) training_size
(C) All above
(D) None of these

💬 Discuss

✅ Correct Answer: (C) All above

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

Q. What is the naive assumption in a Naive Bayes Classifier.

(A) all the classes are independent of each other
(B) all the features of a class are independent of each other
(C) the most probable feature for a class is the most important feature to be cinsidered for classification
(D) all the features of a class are conditionally dependent on each other

💬 Discuss

✅ Correct Answer: (D) all the features of a class are conditionally dependent on each other

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

Q. Data used to optimize the parameter settings of a supervised learner model.

(A) training
(B) test
(C) verification
(D) validation

💬 Discuss

✅ Correct Answer: (D) validation

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

Q. In a real problem, you should check to see if the SVM is separable and then include slack variables if it is not separable.

(A) TRUE
(B) 1 and 2
(C) ---
(D) ---

💬 Discuss

✅ Correct Answer: (B) 1 and 2

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

Q. We usually use feature normalization before using the Gaussian kernel in SVM. What is true about feature normalization?
1. We do feature normalization so that new feature will dominate other
2. Some times, feature normalization is not feasible in case of categorical variables
3. Feature normalization always helps when we use Gaussian kernel in SVM

(A) 1
(B) Small datasets
(C) 1 and 3
(D) 2 and 3

💬 Discuss

✅ Correct Answer: (B) Small datasets

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

Q. The minimum time complexity for training an SVM is O(n2). According to this fact, what sizes of datasets are not best suited for SVM's?

(A) Large datasets
(B) conceptual clustering
(C) Medium sized datasets
(D) Size does not matter

💬 Discuss

✅ Correct Answer: (A) Large datasets

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

Q. 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

💬 Discuss

✅ Correct Answer: (D) n+1

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

Q. 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

💬 Discuss

✅ Correct Answer: (D) 1, 2 and 3

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

Q. Suppose you have fitted a complex regression model on a dataset. Now, you are using Ridge regression with tuning parameter lambda to reduce its complexity. Choose the option(s) below which describes relationship of bias and variance with lambda.

(A) In case of very large lambda; bias is low, variance is low
(B) In case of very large lambda; bias is low, variance is high
(C) In case of very large lambda; bias is high, variance is low
(D) In case of very large lambda; bias is high, variance is high

💬 Discuss

✅ Correct Answer: (C) In case of very large lambda; bias is high, variance is low

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

Q. In SVM, Kernel function is used to map a lower dimensional data into a higher dimensional data.

(A) TRUE
(B) you need to increase the learning rate.
(C) ---
(D) ---

💬 Discuss

✅ Correct Answer: (A) TRUE

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

Machine Learning MCQs with answers Page - 25

Q. The parameter . . . . . . . . allows specifying the percentage of elements to put into the test/training set

Q. What is the naive assumption in a Naive Bayes Classifier.

Q. Data used to optimize the parameter settings of a supervised learner model.

Q. In a real problem, you should check to see if the SVM is separable and then include slack variables if it is not separable.

Q. The minimum time complexity for training an SVM is O(n2). According to this fact, what sizes of datasets are not best suited for SVM's?

Q. 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

Q. Suppose you have fitted a complex regression model on a dataset. Now, you are using Ridge regression with tuning parameter lambda to reduce its complexity. Choose the option(s) below which describes relationship of bias and variance with lambda.

Q. In SVM, Kernel function is used to map a lower dimensional data into a higher dimensional data.

Programming Categories

📱 Download Our App

Machine Learning MCQs with answers Page - 25

Q. The parameter . . . . . . . . allows specifying the percentage of elements to put into the test/training set

Q. What is the naive assumption in a Naive Bayes Classifier.

Q. Data used to optimize the parameter settings of a supervised learner model.

Q. In a real problem, you should check to see if the SVM is separable and then include slack variables if it is not separable.

Q. The minimum time complexity for training an SVM is O(n2). According to this fact, what sizes of datasets are not best suited for SVM's?

Q. 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

Q. Suppose you have fitted a complex regression model on a dataset. Now, you are using Ridge regression with tuning parameter lambda to reduce its complexity. Choose the option(s) below which describes relationship of bias and variance with lambda.

Q. In SVM, Kernel function is used to map a lower dimensional data into a higher dimensional data.

Programming Categories

📱 Download Our App

Q. 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