Machine Learning MCQ Questions

Practice here the most popular Machine Learning MCQ Questions and Answers.

1- What is the most common type of supervised learning?
A) Regression
B) Classification
C) Clustering
D) Dimensionality reduction

2- What is the purpose of a validation set in machine learning?
A) To test the performance of the model on unseen data
B) To train the model
C) To optimize the model’s parameters
D) To select the best model

3- What is the difference between a decision tree and a random forest?
A) A decision tree is a single tree, while a random forest is a collection of decision trees
B) A decision tree is a linear model, while a random forest is a non-linear model
C) A decision tree is a classification model, while a random forest is a regression model
D) A decision tree is a shallow model, while a random forest is a deep model

4- What is the main difference between a supervised and unsupervised learning algorithm?
A) Supervised learning algorithms are trained with labeled data, while unsupervised learning algorithms are trained with unlabeled data
B) Supervised learning algorithms predict continuous values, while unsupervised learning algorithms predict categorical values
C) Supervised learning algorithms are used for regression problems, while unsupervised learning algorithms are used for classification problems
D) Supervised learning algorithms are used for clustering problems, while unsupervised learning algorithms are used for dimensionality reduction

5- What is the main advantage of using a neural network over other machine learning algorithms?
A) Neural networks are able to handle large amounts of complex data
B) Neural networks are able to handle non-linear relationships in the data
C) Neural networks are easy to interpret
D) Neural networks are easy to train

6- What is the main difference between a convolutional neural network (CNN) and a recurrent neural network (RNN)?
A) A CNN is used for image classification, while an RNN is used for natural language processing
B) A CNN has a fixed-size input, while an RNN has a variable-size input
C) A CNN has a feedforward structure, while an RNN has a recursive structure
D) A CNN has a single layer, while an RNN has multiple layers

7- What is the main difference between a generative and discriminative model?
A) A generative model learns the underlying probability distribution of the data, while a discriminative model learns the decision boundary between different classes
B) A generative model is used for classification problems, while a discriminative model is used for regression problems
C) A generative model is unsupervised, while a discriminative model is supervised
D) A generative model is shallow, while a discriminative model is deep

8- What is the main disadvantage of using a support vector machine (SVM) algorithm?
A) SVMs are sensitive to the choice of kernel function
B) SVMs are sensitive to the choice of regularization parameter
C) SVMs are sensitive to the choice of initialization
D) SVMs are sensitive to the choice of optimization algorithm

9- What is the main difference between a bagging and boosting ensemble method?
A) Bagging combines multiple models by averaging their predictions, while boosting combines multiple models by weighting their predictions
B) Bagging is used for classification problems, while boosting is used for regression problems
C) Bagging is used for unsupervised learning, while boosting is used for supervised learning
D) Bagging is used for shallow models, while boosting is used for deep models

Ans for these questions

1. B) Classification
2. A) To test the performance of the model on unseen data
3. A) A decision tree is a single tree, while a random forest is a collection of decision trees
4. A) Supervised learning algorithms are trained with labeled data, while unsupervised learning algorithms are trained with unlabeled data
5. A) Neural networks are able to handle large amounts of complex data
6. A) A CNN is used for image classification, while an RNN is used for natural language processing
7. A) A generative model learns the underlying probability distribution of the data, while a discriminative model learns the decision boundary between different classes
8. A) SVMs are sensitive to the choice of kernel function
9. A) Bagging combines multiple models by averaging their predictions, while boosting combines multiple models by weighting their predictions.