Machine Learning Basics MCQs

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1. Which of the following is a primary goal of supervised learning? A) Discover hidden patterns in data B) Predict outcomes based on labeled data C) Reduce dimensionality of the data D) Identify clusters within the data Answer: B) Predict outcomes based on labeled data 2. In unsupervised learning, the primary task is to: A) Predict future values based on past data B) Classify data into predefined categories C) Discover the underlying structure of the data D) Improve the performance of a predictive model Answer: C) Discover the underlying structure of the data 3. Which of the following is an example of a supervised learning algorithm? A) K-Means Clustering B) Principal Component Analysis (PCA) C) Support Vector Machines (SVM) D) Apriori Algorithm Answer: C) Support Vector Machines (SVM) 4. Which algorithm is commonly used for classification tasks? A) Linear Regression B) K-Means Clustering C) Decision Trees D) Principal Component Analysis (PCA) Answer: C) Decision Trees 5. In which type of machine learning does the model learn from the input and output data? A) Supervised Learning B) Unsupervised Learning C) Reinforcement Learning D) Semi-Supervised Learning Answer: A) 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 Answer: B) To evaluate the performance of the model 7. Which technique is used to prevent overfitting in machine learning models? A) Cross-validation B) Feature Selection C) Regularization D) Data Augmentation Answer: C) 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 Answer: C) The tradeoff between bias and variance in model predictions 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) F1 Score D) R-Squared Answer: C) F1 Score 10. What is the purpose of feature scaling in machine learning? A) To increase the number of features B) To normalize the range of features C) To select important features D) To reduce the dimensionality of the data Answer: B) To normalize the range of features 11. Which algorithm is commonly used for regression tasks? A) K-Means Clustering B) Decision Trees C) Linear Regression D) Naive Bayes Answer: C) Linear Regression 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 Answer: C) Data used to teach the model and learn from 13. Which of the following is a type of unsupervised learning? A) Logistic Regression B) Support Vector Machines C) K-Means Clustering D) Naive Bayes Answer: C) K-Means Clustering 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 Answer: C) When a model performs well on training data but poorly on unseen data 15. Which algorithm is commonly used for dimensionality reduction? A) K-Means Clustering B) Principal Component Analysis (PCA) C) Decision Trees D) Support Vector Machines (SVM) Answer: B) Principal Component Analysis (PCA) 16. In machine learning, what does “feature engineering” involve? A) Designing and selecting features that improve model performance B) Building machine learning models C) Evaluating model performance D) Selecting algorithms for training Answer: A) 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) Gaussian Mixture Models C) Random Forest D) Linear Discriminant Analysis (LDA) Answer: C) Random Forest 18. What is “Gradient Descent”? A) An optimization algorithm used to minimize the cost function B) A method for feature selection C) A technique for model evaluation D) A type of clustering algorithm Answer: A) 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 optimize the parameters of the machine learning algorithm C) To evaluate the performance of the model D) To reduce the dimensionality of the data Answer: B) To optimize the parameters of the machine learning algorithm 20. Which evaluation metric is used to assess regression models? A) Precision B) Recall C) Mean Absolute Error (MAE) D) F1 Score Answer: C) Mean Absolute Error (MAE) 21. In which machine learning problem would you use a “Confusion Matrix”? A) Classification B) Regression C) Clustering D) Dimensionality Reduction Answer: A) Classification 22. What is “Bagging” in ensemble learning? A) Combining multiple models to improve performance B) Selecting a subset of features for each model C) Using different algorithms for each model D) Evaluating model performance Answer: A) Combining multiple models to improve performance 23. Which of the following is a common ensemble method? A) K-Means Clustering B) Principal Component Analysis (PCA) C) Random Forest D) Linear Regression Answer: C) Random Forest 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 method for evaluating model performance by dividing data into multiple folds D) A type of regression analysis Answer: C) 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 Answer: A) Converting data into a specific range 26. Which of the following is NOT a type of machine learning? A) Supervised Learning B) Unsupervised Learning C) Reinforcement Learning D) Static Learning Answer: D) Static Learning 27. What is “Clustering” in machine learning? A) A method for grouping similar data points together B) A technique for regression analysis C) A type of supervised learning D) A method for feature selection Answer: A) A method for grouping similar data points together 28. Which of the following is a popular unsupervised learning algorithm? A) Support Vector Machines (SVM) B) Naive Bayes C) K-Means Clustering D) Decision Trees Answer: C) 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 Answer: A) Choose the most relevant features to improve model performance 30. “Ensemble Learning” combines: A) Multiple models to improve predictive performance B) Different types of data C) Feature selection methods D) Dimensionality reduction techniques Answer: A) Multiple models to improve predictive performance 31. What is the purpose of the “Bias” term in a linear model? A) To adjust the output of the model regardless of the input values B) To scale the features of the model C) To reduce overfitting D) To perform feature selection Answer: A) To adjust the output of the model regardless of the input values 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 Answer: B) Mean Squared Error (MSE) 34. 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) Cross-Entropy Loss D) Euclidean Distance Answer: C) Cross-Entropy Loss 35. What is the main goal of reinforcement learning? A) To find a mapping between input and output B) To discover hidden patterns in data C) To learn a policy that maximizes cumulative reward D) To reduce the dimensionality of the data Answer: C) To learn a policy that maximizes cumulative reward 36. What is the purpose of a “learning rate” in training machine learning models? A) To scale the features of the data B) To control the step size during gradient descent optimization C) To select the best features for the model D) To evaluate model performance Answer: B) To control the step size during gradient descent optimization

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