Classification MCQs January 8, 2026November 18, 2024 by u930973931_answers 15 min Score: 0 Attempted: 0/15 Subscribe 1. What is the primary goal of a classification algorithm in machine learning? (A) To assign each data point to one of the predefined classes or categories (B) To group similar data points into clusters (C) To predict continuous numerical values (D) To reduce the dimensionality of the data 2. Which of the following is a supervised learning algorithm used for classification tasks? (A) K-means clustering (B) Naive Bayes (C) Principal Component Analysis (PCA) (D) DBSCAN 3. Which of the following techniques is commonly used for binary classification? (A) Linear regression (B) K-means clustering (C) Decision trees (D) Apriori algorithm 4. In a confusion matrix, what does the True Positive (TP) represent? (A) The number of instances incorrectly predicted as positive (B) The number of instances correctly predicted as negative (C) The number of instances correctly predicted as positive (D) The number of instances incorrectly predicted as negative 5. What is the accuracy metric in classification? (A) The percentage of false positive instances (B) The percentage of correctly classified instances out of all instances (C) The ratio of True Positives to False Positives (D) The number of features used for classification 6. Which of the following classifiers is based on the Bayes’ Theorem? (A) K-nearest neighbors (KNN) (B) Support Vector Machines (SVM) (C) Naive Bayes (D) Decision Trees 7. In the K-nearest neighbors (KNN) algorithm, how is the class of a new data point determined? (A) By calculating the average of all classes in the dataset (B) By finding the most frequent class among its k nearest neighbors (C) By applying the mean of the features for classification (D) By using a decision tree 8. What is the decision boundary in classification? (A) The point where the algorithm separates training and testing data (B) The boundary between training and validation data (C) The line or surface that separates different classes in the feature space (D) The threshold that classifies data into positive or negative 9. Which of the following algorithms is used for multiclass classification? (A) Logistic Regression (B) Naive Bayes (C) All of the above (D) Decision Trees 10. In logistic regression, the output of the model is a: (A) Continuous value (B) Probability value between 0 and 1 (C) Multiclass output (D) Binary class (0 or 1) 11. What does the F1-score measure in classification tasks? (A) The area under the ROC curve (B) The total number of correct predictions (C) The number of false positives (D) The balance between precision and recall 12. Which of the following is an advantage of Support Vector Machines (SVM) for classification? (A) It works well on both linear and non-linear data (B) It is computationally less expensive than KNN (C) It requires fewer training examples (D) It can only handle binary classification tasks 13. What is overfitting in a classification model? (A) The model performs well on both the training and testing data (B) The model cannot handle new, unseen data (C) The model fails to recognize patterns in the data (D) The model performs poorly on the testing data but well on training data 14. Which metric is used to evaluate binary classification models when there is a class imbalance? (A) Accuracy (B) Precision (C) Area under the ROC curve (AUC-ROC) (D) Recall 15. What is cross-validation used for in classification tasks? (A) To assess the model’s generalizability and avoid overfitting (B) To improve the classification accuracy (C) To find the optimal number of features (D) To select the best classification algorithm