Machine Learning MCQs

#1. Which of the following is not a type of machine learning algorithm?

Unsupervised learning

Reinforcement learning

Supervised learning

Semi-supervised learning

Self-learning

#2. What is the purpose of a validation set in machine learning?

To evaluate the model on data it has never seen before

To test the model on the training data

To fine-tune hyperparameters

To select features for the model

To train the model

#3. Which algorithm is commonly used for classification problems in machine learning?

K-means clustering

Decision trees

Principal Component Analysis (PCA)

Linear regression

Apriori algorithm

#4. What is overfitting in machine learning?

When the model performs well on the training data but poorly on new, unseen data

When the model performs poorly on the training data but well on new, unseen data

When the model performs equally well on both training and test data

When the model is too simple to capture the underlying patterns in the data

When the model has too few parameters

#5. Which activation function is commonly used in the output layer of a binary classification neural network?

Sigmoid

ReLU

Tanh

Softmax

Linear

#6. What is a hyperparameter in machine learning?

A parameter set prior to training and remains constant during training

A parameter that determines the number of features in the dataset

A parameter learned by the model during training

A parameter that determines the number of training samples

A parameter that is only relevant for unsupervised learning

#7. In which phase of the machine learning process is feature engineering typically performed?

Data preprocessing

Model training

Model evaluation

Model deployment

Data collection

#8. Which algorithm is used for dimensionality reduction in machine learning?

Principal Component Analysis (PCA)

Random Forest

K-Nearest Neighbors (KNN)

Gradient Descent

Support Vector Machine (SVM)

#9. What is the purpose of regularization in machine learning?

To prevent overfitting

To increase the complexity of the model

To decrease the complexity of the model

To speed up the training process

To add noise to the data

#10. Which evaluation metric is commonly used for regression problems in machine learning?

Mean Absolute Error (MAE)

Accuracy

F1-score

Precision

Recall

#11. Which algorithm is used for anomaly detection in machine learning?

Isolation Forest

Logistic Regression

K-Means Clustering

Decision Trees

Support Vector Machines (SVM)

#12. What is the purpose of the bias term in a neural network?

To shift the activation function to the left or right

To reduce overfitting

To increase model complexity

To regularize the model

To add non-linearity

#13. What is the goal of unsupervised learning?

Discovering hidden patterns or structures in data

Maximizing prediction accuracy

Minimizing model complexity

Minimizing training time

Classifying data into predefined categories

#14. Which method is commonly used to handle missing data in a dataset?

Imputation

Deletion

Ignoring it during training

Normalization

Standardization

#15. What is the purpose of the Adam optimizer in deep learning?

A stochastic gradient descent optimization algorithm

A clustering algorithm

A dimensionality reduction technique

A regularization technique

A feature extraction method

#16. In reinforcement learning, what is the agent’s objective?

To maximize the cumulative reward over time

To minimize the cumulative reward over time

To memorize the training data

To predict future states

To classify data into predefined categories

#17. Which technique is used to reduce the risk of overfitting in decision trees?

Pruning

Grafting

Bagging

Boosting

Random selection of features

#18. What does the term “bias-variance tradeoff” refer to in machine learning?

The balance between model complexity and the amount of training data

The balance between underfitting and overfitting

The tradeoff between the accuracy of a model and its interpretability

The tradeoff between bias (error due to too simple model) and variance (error due to too complex model)

The tradeoff between the number of features and the number of samples in the dataset

#19. What is the purpose of a confusion matrix in classification problems?

To visualize the performance of a model

To display the distribution of classes in a dataset

To evaluate the quality of features

To quantify the performance of a classification model

To calculate the probability of class membership

#20. Which algorithm is commonly used for collaborative filtering in recommendation systems?

Matrix Factorization