Machine Learning Basics MCQs January 8, 2026August 9, 2024 by u930973931_answers 35 min Score: 0 Attempted: 0/35 Subscribe 1. Which of the following is a primary goal of supervised learning? (A) Predict outcomes based on labeled data (B) Discover hidden patterns in data (C) Reduce dimensionality of the data (D) Identify clusters within the data 2. In unsupervised learning, the primary task is to: (A) Discover the underlying structure of the data (B) Classify data into predefined categories (C) Predict future values based on past data (D) Improve the performance of a predictive model 3. Which of the following is an example of a supervised learning algorithm? (A) K-Means Clustering (B) Support Vector Machines (SVM) (C) Principal Component Analysis (PCA) (D) Apriori Algorithm 4. Which algorithm is commonly used for classification tasks? (A) Linear Regression (B) K-Means Clustering (C) Decision Trees (D) Principal Component Analysis (PCA) 5. In which type of machine learning does the model learn from the input and output data? (A) Reinforcement Learning (B) Unsupervised Learning (C) Supervised Learning (D) Semi-Supervised Learning 6. What is the main purpose of cross-validation in machine learning? (A) To split the data into training and testing sets (B) To evaluate the performance of the model (C) To select the best features for the model (D) To increase the size of the dataset 7. Which technique is used to prevent overfitting in machine learning models? (A) Cross-validation (B) Feature Selection (C) Data Augmentation (D) Regularization 8. What does the “bias-variance tradeoff” refer to? (A) The balance between training and test data (B) The balance between the complexity of the model and its performance (C) The tradeoff between bias and variance in model predictions (D) The tradeoff between data preprocessing and model training 9. Which of the following is a metric used to evaluate classification models? (A) Mean Squared Error (MSE) (B) Root Mean Squared Error (RMSE) (C) R-Squared (D) F1 Score 10. What is the purpose of feature scaling in machine learning? (A) To increase the number of features (B) To reduce the dimensionality of the data (C) To select important features (D) To normalize the range of features 11. Which algorithm is commonly used for regression tasks? (A) K-Means Clustering (B) Linear Regression (C) Decision Trees (D) Naive Bayes 12. In the context of machine learning, what is “training data”? (A) Data used to evaluate the performance of a model (B) Data used to make predictions (C) Data used to teach the model and learn from (D) Data used to select the best features 13. Which of the following is a type of unsupervised learning? (A) Logistic Regression (B) K-Means Clustering (C) Support Vector Machines (D) Naive Bayes 14. What is “Overfitting” in machine learning? (A) When a model performs well on unseen data (B) When a model performs poorly on training data (C) When a model performs well on training data but poorly on unseen data (D) When a model has too few features 15. Which algorithm is commonly used for dimensionality reduction? (A) K-Means Clustering (B) Decision Trees (C) Principal Component Analysis (PCA) (D) Support Vector Machines (SVM) 16. In machine learning, what does “feature engineering” involve? (A) Selecting algorithms for training (B) Building machine learning models (C) Evaluating model performance (D) Designing and selecting features that improve model performance 17. Which of the following is an example of a supervised learning algorithm used for classification? (A) K-Means Clustering (B) Random Forest (C) Gaussian Mixture Models (D) Linear Discriminant Analysis (LDA) 18. What is “Gradient Descent”? (A) A type of clustering algorithm (B) A method for feature selection (C) A technique for model evaluation (D) An optimization algorithm used to minimize the cost function 19. What is the purpose of “Hyperparameter Tuning”? (A) To select the best features for the model (B) To evaluate the performance of the model (C) To optimize the parameters of the machine learning algorithm (D) To reduce the dimensionality of the data 20. Which evaluation metric is used to assess regression models? (A) Mean Absolute Error (MAE) (B) Recall (C) Precision (D) F1 Score 21. In which machine learning problem would you use a “Confusion Matrix”? (A) Dimensionality Reduction (B) Regression (C) Clustering (D) Classification 22. What is “Bagging” in ensemble learning? (A) Using different algorithms for each model (B) Selecting a subset of features for each model (C) Combining multiple models to improve performance (D) Evaluating model performance 23. Which of the following is a common ensemble method? (A) K-Means Clustering (B) Random Forest (C) Principal Component Analysis (PCA) (D) Linear Regression 24. In the context of machine learning, what is “Cross-Validation”? (A) A technique for selecting features (B) A method for splitting data into training and testing sets (C) A type of regression analysis (D) A method for evaluating model performance by dividing data into multiple folds 25. What does “Normalization” refer to in data preprocessing? (A) Converting data into a specific range (B) Removing outliers from the data (C) Adding noise to the data (D) Reducing the number of features 26. Which of the following is NOT a type of machine learning? (A) Static Learning (B) Unsupervised Learning (C) Reinforcement Learning (D) Supervised Learning 27. What is “Clustering” in machine learning? (A) A technique for regression analysis (B) A method for grouping similar data points together (C) A type of supervised learning (D) A method for feature selection 28. Which of the following is a popular unsupervised learning algorithm? (A) Support Vector Machines (SVM) (B) Naive Bayes (C) Decision Trees (D) K-Means Clustering 29. In machine learning, “Feature Selection” is used to: (A) Choose the most relevant features to improve model performance (B) Increase the number of features in the dataset (C) Normalize the features (D) Reduce the size of the dataset 30. “Ensemble Learning” combines: (A) Feature selection methods (B) Different types of data (C) Multiple models to improve predictive performance (D) Dimensionality reduction techniques 31. What is the purpose of the “Bias” term in a linear model? (A) To scale the features of the model (B) To adjust the output of the model regardless of the input values (C) To reduce overfitting (D) To perform feature selection 32. Which of the following is a common loss function used in regression problems? (A) Cross-Entropy Loss (B) Mean Squared Error (MSE) (C) Hinge Loss (D) Gini Index 33. Which of the following is an example of a loss function used in classification tasks? (A) Mean Squared Error (MSE) (B) Mean Absolute Error (MAE) (C) Euclidean Distance (D) Cross-Entropy Loss 34. What is the main goal of reinforcement learning? (A) To learn a policy that maximizes cumulative reward (B) To discover hidden patterns in data (C) To find a mapping between input and output (D) To reduce the dimensionality of the data 35. What is the purpose of a “learning rate” in training machine learning models? (A) To control the step size during gradient descent optimization (B) To scale the features of the data (C) To select the best features for the model (D) To evaluate model performance