AI Model Evaluation and Metrics: Understanding Performance Indicators MCQs

Explore key concepts in accuracy, precision, recall and model assessment techniques. Ideal for students and AI professionals.

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1. What does accuracy measure in a classification model?
  • The ratio of false positives to false negatives.
  • The difference between predicted and actual values.
  • The percentage of correct predictions.
  • The ability of the model to handle imbalanced data.
2. Which metric is used to evaluate a model’s ability to correctly classify positive instances?
  • Precision
  • Recall
  • Accuracy
  • F1-Score
3. What is precision in the context of classification models?
  • The ability of the model to reduce errors.
  • The percentage of correct predictions among all predictions.
  • The ability to identify negative instances.
  • The percentage of true positive predictions among all positive predictions.
4. What does recall measure in a classification model?
  • The percentage of actual positive instances correctly identified by the model.
  • The percentage of true positive predictions among all negative predictions.
  • The ability to handle missing data.
  • The overall accuracy of the model.
5. What is F1-Score?
  • The sum of precision and recall.
  • The harmonic mean of precision and recall.
  • The difference between precision and recall.
  • A measure of model complexity.
6. What is the confusion matrix used for?
  • To calculate the model’s execution time.
  • To train the model on labeled data.
  • To visualize the distribution of the dataset.
  • To evaluate the performance of a classification model by comparing predicted and actual values.
7. What does the term 'false positive' refer to in classification models?
  • When the model incorrectly classifies a negative instance as positive.
  • When the model correctly identifies a positive instance.
  • When the model misses positive instances.
  • When the model predicts the correct label for negative instances.
8. What is ROC (Receiver Operating Characteristic) curve used for?
  • To calculate the accuracy of a model.
  • To plot the true positive rate against the false positive rate.
  • To determine the number of clusters in unsupervised learning.
  • To visualize the decision boundaries of the model.
9. What does the AUC (Area Under Curve) represent in an ROC curve?
  • The overall ability of the model to distinguish between positive and negative classes.
  • The ratio of true positives to total predictions.
  • The proportion of the dataset used for testing.
  • The time complexity of the model.
10. Which metric is primarily used to evaluate regression models?
  • Precision
  • F1-Score
  • Mean Squared Error (MSE)
  • Confusion Matrix
11. What does R-squared (R²) indicate in regression analysis?
  • The proportion of variance in the dependent variable explained by the independent variables.
  • The total number of errors in a regression model.
  • The relationship between independent and dependent variables.
  • The total error of a classification model.
12. What is the main advantage of using cross-validation during model evaluation?
  • It increases the model’s training time.
  • It helps in assessing the model’s performance by using different subsets of data for training and testing.
  • It reduces the computational power required.
  • It helps in overfitting the model.
13. What is a characteristic of a model that is overfitting?
  • It performs well on the training data but poorly on unseen data.
  • It performs well on both training and test data.
  • It fails to learn from the training data.
  • It consistently gives inaccurate results.
14. What is the primary goal of model selection in machine learning?
  • To minimize the execution time of the model.
  • To select the model that performs best on the training set.
  • To choose the model that generalizes well on unseen data.
  • To maximize the number of features in the dataset.
15. What does the term "underfitting" mean in model evaluation?
  • When a model is too simple and fails to capture the underlying patterns of the data.
  • When a model is overly complex and learns too much from the training data.
  • When the model performs well on unseen data but not on the training data.
  • When the model is unable to make predictions on any data.
16. What is a key benefit of using the F1-score over precision and recall individually?
  • It is more useful for regression problems.
  • It is easier to compute than precision and recall.
  • It balances the tradeoff between precision and recall in one metric.
  • It is applicable only to binary classification problems.
17. Which of the following is a limitation of using accuracy as the only evaluation metric for imbalanced data?
  • Accuracy can be misleading because it may favor the majority class.
  • Accuracy always provides a clear picture of model performance.
  • Accuracy ignores the false negatives in the dataset.
  • Accuracy is not suitable for regression problems.
18. In which scenario would you use the area under the precision-recall curve (PR AUC)?
  • When there is a need for a visual representation of errors.
  • When there are only two possible outcomes in a classification task.
  • When evaluating models with highly imbalanced classes.
  • When comparing regression models with similar performance.
19. What is a characteristic of a good evaluation metric for a machine learning model?
  • It should be easy to compute with minimal data.
  • It should only consider the performance on the training data.
  • It should reflect the real-world performance of the model on unseen data.
  • It should always yield the same result for different datasets.
20. Which of the following best describes the importance of model evaluation?
  • It ensures that the model is both accurate and generalizes well to new data.
  • It only measures the speed of the model during execution.
  • It focuses solely on optimizing the model for training data.
  • It is only useful for determining model performance on training sets.
21. Which of the following is an example of a metric for evaluating classification models in imbalanced datasets?
  • F1-Score
  • Mean Squared Error
  • ROC Curve
  • R-squared
22. What is the purpose of the log-loss function in classification problems?
  • To normalize the dataset.
  • To compute the time complexity of the model.
  • To evaluate the performance of a model based on the probability of its predictions.
  • To calculate the variance of the errors.
23. Which of the following is NOT a disadvantage of using accuracy as a performance metric for imbalanced data?
  • It may be misleading if the dataset has a large class imbalance.
  • It doesn't account for the types of errors made by the model.
  • It is insensitive to false negatives.
  • It always works well for binary classification tasks.
24. Which metric is used to evaluate a classification model on multi-class problems?
  • Multi-class ROC-AUC
  • Mean Squared Error
  • Adjusted R-squared
  • Precision-Recall Curve
25. What does the term "overfitting" mean in model evaluation?
  • The model is optimized for speed but not accuracy.
  • The model fails to capture the patterns in the training data.
  • The model performs well on both training and test data.
  • The model learns noise from the training data and performs poorly on unseen data.
26. Which of the following is true about the precision-recall curve?
  • It only applies to binary classification tasks.
  • It is used primarily for regression problems.
  • It shows the tradeoff between precision and recall for different thresholds.
  • It is used to evaluate the accuracy of the dataset.
27. What does "area under the ROC curve" (AUC-ROC) measure in a classification model?
  • The ability of the model to distinguish between positive and negative classes.
  • The accuracy of the model on the training data.
  • The model's error rate in predictions.
  • The precision of the model on unseen data.
28. What does the term "false negatives" refer to in the confusion matrix?
  • Instances where the model correctly classifies a positive instance as positive.
  • Instances where the model correctly classifies a negative instance as negative.
  • Instances where the model incorrectly classifies a positive instance as negative.
  • Instances where the model incorrectly classifies a negative instance as positive.
29. Which evaluation metric is most appropriate for a binary classification problem with an imbalanced dataset?
  • Precision-Recall AUC
  • R-squared
  • Mean Absolute Error
  • Confusion Matrix
30. What does the term "true positives" mean in a confusion matrix?
  • Instances where the model correctly classifies positive instances.
  • Instances where the model incorrectly classifies positive instances.
  • Instances where the model correctly classifies negative instances.
  • Instances where the model incorrectly classifies negative instances.

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