Machine Learning MCQ (Multiple Choice Questions)

Here are 1000 MCQs on Machine Learning (Chapterwise).

1. What is Machine learning?
a) The selective acquisition of knowledge through the use of computer programs
b) The selective acquisition of knowledge through the use of manual programs
c) The autonomous acquisition of knowledge through the use of computer programs
d) The autonomous acquisition of knowledge through the use of manual programs
View Answer

Answer: c
Explanation: Machine learning is the autonomous acquisition of knowledge through the use of computer programs.

2. K-Nearest Neighbors (KNN) is classified as what type of machine learning algorithm?
a) Instance-based learning
b) Parametric learning
c) Non-parametric learning
d) Model-based learning
View Answer

Answer: a
Explanation: KNN doesn’t build a parametric model of the data. Instead, it directly classifies new data points based on the k nearest points in the training data.

3. Which of the following is not a supervised machine learning algorithm?
a) K-means
b) Naïve Bayes
c) SVM for classification problems
d) Decision tree
View Answer

Answer: a
Explanation: Decision tree, SVM (Support vector machines) for classification problems and Naïve Bayes are the examples of supervised machine learning algorithm. K-means is an example of unsupervised machine learning algorithm.
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4. What’s the key benefit of using deep learning for tasks like recognizing images?
a) They need less training data than other methods.
b) They’re easier to explain and understand than other models.
c) They can learn complex details from the data on their own.
d) They work faster and are more efficient computationally.
View Answer

Answer: c
Explanation: Deep learning is great at figuring out intricate details from data, especially in tasks like recognizing images.

5. Which algorithm is best suited for a binary classification problem?
a) K-nearest Neighbors
b) Decision Trees
c) Random Forest
d) Linear Regression
View Answer

Answer: b
Explanation: Decision Trees are versatile and can be used for classification problems, particularly for binary classification, where the output is divided into two classes.

6. What is the key difference between supervised and unsupervised learning?
a) Supervised learning requires labeled data, while unsupervised learning does not.
b) Supervised learning predicts labels, while unsupervised learning discovers patterns.
c) Supervised learning is used for classification, while unsupervised learning is used for regression.
d) Supervised learning is always more accurate than unsupervised learning.
View Answer

Answer: a
Explanation: The presence or absence of labeled data in the training set distinguishes supervised and unsupervised learning approaches.

7. Which type of machine learning algorithm falls under the category of “unsupervised learning”?
a) Linear Regression
b) K-means Clustering
c) Decision Trees
d) Random Forest
View Answer

Answer: b
Explanation: K-means Clustering is an example of unsupervised learning used for clustering unlabeled data based on similarities.
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8. Which of the following statements is true about AdaBoost?
a) It is particularly prone to overfitting on noisy datasets
b) Complexity of the weak learner is important in AdaBoost
c) It is generally more prone to overfitting
d) It improves classification accuracy
View Answer

Answer: c
Explanation: AdaBoost is generally not more prone to overfitting but is less prone to overfitting. And it is prone to overfitting on noisy datasets. If you use very simple weak learners, then the algorithms are much less prone to overfitting and it improves classification accuracy. So Complexity of the weak learner is important in AdaBoost.

9. Which one of the following models is a generative model used in machine learning?
a) Support vector machines
b) Naïve Bayes
c) Logistic Regression
d) Linear Regression
View Answer

Answer: b
Explanation: Naïve Bayes is a type of generative model which is used in machine learning. Linear Regression, Logistic Regression and Support vector machines are the types of discriminative models which are used in machine learning.
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10. An artificially intelligent car decreases its speed based on its distance from the car in front of it. Which algorithm is used?
a) Naïve-Bayes
b) Decision Tree
c) Linear Regression
d) Logistic Regression
View Answer

Answer: c
Explanation: The output is numerical. It determines the speed of the car. Hence it is not a classification problem. All the three, decision tree, naïve-Bayes, and logistic regression are classification algorithms. Linear regression, on the other hand, outputs numerical values based on input. So, this can be used.

11. Which of the following statements is false about Ensemble learning?
a) It is a supervised learning algorithm
b) It is an unsupervised learning algorithm
c) More random algorithms can be used to produce a stronger ensemble
d) Ensembles can be shown to have more flexibility in the functions they can represent
View Answer

Answer: b
Explanation: Ensemble learning is not an unsupervised learning algorithm. It is a supervised learning algorithm that combines several machine learning techniques into one predictive model to decrease variance and bias. It can be trained and then used to make predictions. And this ensemble can be shown to have more flexibility in the functions they can represent.

12. Which of the following statements is true about stochastic gradient descent?
a) It processes one training example per iteration
b) It is not preferred, if the number of training examples is large
c) It processes all the training examples for each iteration of gradient descent
d) It is computationally very expensive, if the number of training examples is large
View Answer

Answer: a
Explanation: Stochastic gradient descent processes one training example per iteration. That is it updates the weight vector based on one data point at a time. All other three are the features of Batch Gradient Descent.

13. Decision tree uses the inductive learning machine learning approach.
a) False
b) True
View Answer

Answer: b
Explanation: Decision tree uses the inductive learning machine learning approach. Inductive learning enables the system to recognize patterns and regularities in previous knowledge or training data and extract the general rules from them. A decision tree is considered to be an inductive learning task as it uses particular facts to make more generalized conclusions.

14. What elements describe the Candidate-Elimination algorithm?
a) depends on the dataset
b) just a set of candidate hypotheses
c) just a set of instances
d) set of instances, set of candidate hypotheses
View Answer

Answer: d
Explanation: A set of instances is required. A set of candidate hypotheses are given. These are applied to the training data and the list of accurate hypotheses is output in accordance with the candidate-elimination algorithm.

15. Which of the following statements is not true about boosting?
a) It mainly increases the bias and the variance
b) It tries to generate complementary base-learners by training the next learner on the mistakes of the previous learners
c) It is a technique for solving two-class classification problems
d) It uses the mechanism of increasing the weights of misclassified data in preceding classifiers
View Answer

Answer: a
Explanation: Boosting does not increase the bias and variance but it mainly reduces the bias and the variance. It is a technique for solving two-class classification problems. And it tries to generate complementary base-learners by training the next learner (by increasing the weights) on the mistakes (misclassified data) of the previous learners.


Chapterwise Multiple Choice Questions on Machine Learning

Machine Learning MCQ - Multiple Choice Questions and Answers

Our 1000+ MCQs focus on all topics of the Machine Learning subject, covering 100+ topics. This will help you to prepare for exams, contests, online tests, quizzes, viva-voce, interviews, and certifications. You can practice these MCQs chapter by chapter starting from the 1st chapter or you can jump to any chapter of your choice.
  1. Formal Learning Model
  2. Version Spaces
  3. VC-Dimension
  4. Linear Regression
  5. Multivariate Linear Regression
  6. Logistic Regression
  7. Ensemble Learning
  8. Stochastic Gradient Descent
  9. Kernels
  10. Support Vector Machines
  11. Decision Trees
  12. Nearest Neighbor
  13. Naive-Bayes Algorithm
  14. Machine Learning – Neural Networks

1. MCQ on Formal Learning Model

The section contains multiple choice questions and answers on statistical learning framework, empirical minimization framework and PAC learning.

  • Statistical Learning Framework
  • Empirical Minimization Framework
  • PAC Learning
  • 2. Machine Learning MCQ on Version Spaces

    The section contains questions and answers on version spaces, find-s algorithm and candidate elimination algorithm.

  • Version Spaces
  • Find-S Algorithm
  • Candidate Elimination Algorithm
  • 3. Machine Learning Multiple Choice Questions on VC-Dimension

    The section contains Machine Learning MCQs on VC-dimension and the Fundamental Theorem of PAC Learning.

  • VC-Dimension
  • VC-Dimension – Set 2
  • Fundamental Theorem of PAC Learning
  • 4. MCQ on Linear Regression

    The section contains Machine Learning multiple choice questions and answers on linear regression in machine learning, linear regression cost functions, and gradient descent.

  • Linear Regression in Machine Learning
  • Linear Regression – Cost Function
  • Linear Regression – Gradient Descent
  • 5. Machine Learning MCQ on Multivariate Linear Regression

    The section contains Machine Learning questions and answers on multivariate linear regression, gradient descent for multiple variables, and polynomial regression.

  • Multivariate Linear Regression
  • Gradient Descent for Multiple Variables
  • Polynomial Regression in Machine learning
  • 6. Machine Learning Multiple Choice Questions on Logistic Regression

    This section features MCQs on logistic regression, hypothesis representation, decision boundary, cost function and gradient descent, logistic regression for multiple classification, and advanced optimization.

  • Logistic Regression
  • Hypothesis Representation
  • Logistic Regression – Decision Boundary
  • Logistic Regression – Cost Function and Gradient Descent
  • Logistic Regression – Advanced Optimization
  • Logistic Regression – Multiple Classification
  • 7. MCQ on Ensemble Learning

    The section contains multiple choice questions and answers on ensemble learning, covering error-correcting output codes, model combination schemes, boosting weak learnability, the AdaBoost algorithm, and stacking.

  • Ensemble Learning – Model Combination Schemes
  • Ensemble Learning
  • Error Correcting Output Codes
  • Boosting Weak Learnability
  • Adaboost Algorithm
  • Stacking
  • 8. Machine Learning MCQ on Stochastic Gradient Descent

    The section contains questions and answers on optimization algorithms, specifically focusing on Stochastic Gradient Descent (SGD), its variants, the standard Gradient Descent Algorithm, and Subgradient Descent.

  • Gradient Descent Algorithm
  • Subgradient Descent
  • Stochastic Gradient Descent
  • Stochastic Gradient Descent – Set 2
  • SGD Variants
  • 9. Machine Learning Multiple Choice Questions on Kernels

    The section contains Machine Learning MCQs on kernels and kernel trick.

  • Kernels
  • Kernel Trick
  • 10. MCQ on Support Vector Machines

    The section contains multiple choice questions and answers on support vector machines (SVMs), covering key concepts like the large margin intuition, margins and hard/soft SVMs, norm regularization, optimality conditions and support vectors, and finally, implementing soft SVMs using Stochastic Gradient Descent (SGD).

  • Support Vector Machines
  • Large Margin Intuition
  • Margin and Hard SVM
  • Soft SVM and Norm Regularization
  • Optimality Conditions and Support Vectors
  • Implementing Soft SVM with SGD
  • 11. Machine Learning MCQ on Decision Trees

    The section contains questions and answers on decision trees, covering core concepts such as decision tree pruning, inductive bias, classification trees, regression trees, and the powerful Random Forest algorithm.

  • Decision Trees
  • Decision Trees – Gain Measure Implementation
  • Decision Tree Pruning
  • Decision Tree Pruning – Set 2
  • Decision Trees – Threshold Based Splitting Rules
  • Decision Trees – Inductive Bias
  • Classification Tree
  • Regression Trees
  • Random Forest Algorithm
  • 12. Machine Learning Multiple Choice Questions on Nearest Neighbor

    The section contains MCQs on K-Nearest Neighbor Algorithm and Nearest Neighbor Analysis.

  • K-Nearest Neighbor Algorithm
  • Nearest Neighbor Analysis
  • 13. MCQ on Naive-Bayes Algorithm

    The section contains multiple choice questions and answers on Naive-Bayes Algorithm.

  • Naive-Bayes Algorithm
  • 14. Neural Networks in Machine Learning

    The section contains multiple choice questions and answers on nonlinear hypothesis, neurons and the brain, model representation, multiclass classification, cost function, gradient checking, and random initialization.

  • Backpropagation Algorithm
  • Backpropagation Algorithm – 2
  • Backpropagation Algorithm – 3
  • Non-Linear Hypothesis
  • Neurons and the Brain
  • Model Representation
  • Multiclass Classification
  • Cost Function
  • Gradient Checking
  • Random Initialization
  • If you would like to learn "Machine Learning" thoroughly, you should attempt to work on the complete set of 1000+ MCQs - multiple choice questions and answers mentioned above. It will immensely help anyone trying to crack an exam or an interview.

    Wish you the best in your endeavor to learn and master Machine Learning!

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    Manish Bhojasia, a technology veteran with 20+ years @ Cisco & Wipro, is Founder and CTO at Sanfoundry. He lives in Bangalore, and focuses on development of Linux Kernel, SAN Technologies, Advanced C, Data Structures & Alogrithms. Stay connected with him at LinkedIn.

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