GATE 2025 Machine Learning MCQs

Question 1: What is the main goal of machine learning?
a) To make computers intelligent
b) To automate manual tasks
c) To enable computers to learn from data
d) To create self-aware machines

Answer: c) To enable computers to learn from data


Question 2: In machine learning, what is a model?
a) A physical machine that performs computations
b) A representation of the data
c) A set of training examples
d) A type of computer algorithm

Answer: b) A representation of the data


Question 3: Which of the following is a supervised learning task?
a) Clustering
b) Reinforcement learning
c) Dimensionality reduction
d) Classification

Answer: d) Classification


Question 4: Which evaluation metric is commonly used for classification tasks when class imbalance is present?
a) Mean Squared Error (MSE)
b) Accuracy
c) F1-score
d) R-squared

Answer: c) F1-score


Question 5: What is the purpose of the validation set in machine learning?
a) To train the model
b) To fine-tune hyperparameters
c) To test the model's generalization
d) To provide additional training data

Answer: b) To fine-tune hyperparameters


Question 6: Which type of machine learning algorithm aims to mimic the process of human learning?
a) Supervised learning
b) Unsupervised learning
c) Reinforcement learning
d) Deep learning

Answer: c) Reinforcement learning


Question 7: What does the term "overfitting" refer to in machine learning?
a) When a model performs well on the training data but poorly on new data
b) When a model performs well on new data but poorly on the training data
c) When a model perfectly fits the training data
d) When a model is too simple to capture the underlying patterns


Answer: a) When a model performs well on the training data but poorly on new data


Question 8: Which machine learning algorithm is suitable for solving regression problems?
a) K-Means clustering
b) Random Forest
c) K-Nearest Neighbors (KNN)
d) Apriori algorithm

Answer: b) Random Forest


Question 9: Which technique is used to reduce the dimensionality of data while preserving as much information as possible?
a) Clustering
b) Feature extraction
c) Feature selection
d) Regularization

Answer: b) Feature extraction


Question 10: What is the purpose of the bias term in a linear regression model?
a) To handle outliers
b) To avoid overfitting
c) To model the noise in the data
d) To shift the regression line up or down

Answer: d) To shift the regression line up or down


Question 11: Which algorithm is used for finding frequent itemsets in transactional databases?
a) Decision Trees
b) K-Means clustering
c) Apriori algorithm
d) Support Vector Machines (SVM)

Answer: c) Apriori algorithm


Question 12: In the context of machine learning, what is the term "bias-variance trade-off" referring to?
a) The trade-off between the quality of training data and testing data
b) The trade-off between the complexity of a model and its ability to generalize
c) The trade-off between the number of features and the size of the dataset
d) The trade-off between precision and recall in classification

Answer: b) The trade-off between the complexity of a model and its ability to generalize


Question 13: Which algorithm is used for hierarchical clustering?
a) K-Means clustering
b) Agglomerative clustering
c) DBSCAN
d) Principal Component Analysis (PCA)

Answer: b) Agglomerative clustering


Question 14: Which method can be used to handle missing data in a dataset?
a) Removing the entire column with missing data
b) Replacing missing data with the median value of the column
c) Ignoring the rows with missing data during analysis
d) All of the above

Answer: d) All of the above


Question 15: In a neural network, what are the layers between the input and output layers called?
a) Hidden layers
b) Output layers
c) Feature layers
d) Input layers

Answer: a) Hidden layers


Question 16: Which optimization algorithm is commonly used to update the weights of neural networks during training?
a) Gradient Descent
b) K-Means
c) Apriori algorithm
d) Decision Trees

Answer: a) Gradient Descent


Question 17: Which technique is used to combat the vanishing gradient problem in deep neural networks?
a) ReLU activation function
b) Sigmoid activation function
c) Tanh activation function
d) Batch normalization

Answer: a) ReLU activation function


Question 18: Which machine learning algorithm is inspired by the functioning of the human brain's neural networks?
a) K-Means clustering
b) Decision Trees
c) Support Vector Machines (SVM)
d) Artificial Neural Networks

Answer: d) Artificial Neural Networks


Question 19: Which ensemble learning method combines multiple weak learners to create a strong learner?
a) K-Means clustering
b) Decision Trees
c) Gradient Boosting
d) K-Nearest Neighbors (KNN)

Answer: c) Gradient Boosting


Question 20: In the context of Support Vector Machines (SVM), what is the "kernel trick"?
a) A technique to add more features to the dataset
b) A method to increase the regularization term
c) A way to perform dimensionality reduction
d) A way to implicitly map data to higher-dimensional spaces

Answer: d) A way to implicitly map data to higher-dimensional spaces


Question 21: Which type of learning is characterized by an agent learning through interactions with an environment and receiving rewards?
a) Supervised learning
b) Unsupervised learning
c) Reinforcement learning
d) Semi-supervised learning

Answer: c) Reinforcement learning


Question 22: What is the primary goal of feature scaling in machine learning?
a) To convert categorical features into numerical features
b) To normalize the feature values to a standard range
c) To create new features from existing ones
d) To reduce the number of features in the dataset

Answer: b) To normalize the feature values to a standard range


Question 23: Which machine learning algorithm is sensitive to the scale of features and requires feature scaling?
a) Decision Trees
b) K-Means clustering
c) Support Vector Machines (SVM)
d) Naive Bayes

Answer: c) Support Vector Machines (SVM)


Question 24: In a k-fold cross-validation, how is the dataset divided?
a) It is divided into \(k\) equal subsets
b) It is divided into \(k-1\) training subsets and 1 validation subset
c) It is divided into training and testing subsets based on a ratio
d) It is divided randomly into disjoint subsets

Answer: b) It is divided into \(k-1\) training subsets and 1 validation subset


Question 25: Which technique is used to reduce the impact of noise and outliers in a dataset?
a) Feature extraction
b) Regularization
c) Cross-validation
d) Principal Component Analysis (PCA)

Answer: b) Regularization


Question 26: Which algorithm is used for finding the optimal clustering of data points?
a) Random Forest
b) K-Means clustering
c) Support Vector Machines (SVM)
d) Hierarchical clustering

Answer: b) K-Means clustering


Question 27: What is the primary purpose of a decision tree's leaf nodes?
a) To make predictions
b) To split the data
c) To represent features
d) To store feature values

Answer: a) To make predictions


Question 28: Which machine learning approach is based on the assumption that similar data points are more likely to have the same labels?
a) Clustering
b) Classification
c) Regression
d) Anomaly detection

Answer: a) Clustering


Question 29: In a precision-recall curve, which axis represents precision?
a) Horizontal axis
b) Vertical axis
c) Both axes equally
d) None of the above


Answer: b) Vertical axis

Question 30: What is the purpose of regularization in linear regression?
a) To make the model more complex
b) To avoid underfitting
c) To encourage overfitting
d) To reduce the complexity of the model

Answer: b) To avoid underfitting