Multiple Choice Questions on Model Evaluation in AI

📚 Quick Study Guide

• 📏 Accuracy: Measures the proportion of correctly classified instances. It's calculated as $\frac{True Positives + True Negatives}{Total Instances}$.
• 🎯 Precision: Indicates how many of the instances predicted as positive are actually positive. Formula: $\frac{True Positives}{True Positives + False Positives}$.
• ✅ Recall (Sensitivity): Shows how many of the actual positive instances were correctly identified. Formula: $\frac{True Positives}{True Positives + False Negatives}$.
• ⚖️ F1-Score: The harmonic mean of precision and recall, balancing both. Calculated as $2 * \frac{Precision * Recall}{Precision + Recall}$.
• 📊 Confusion Matrix: A table that visualizes the performance of a classification model by showing True Positives, True Negatives, False Positives, and False Negatives.
• 📉 ROC Curve & AUC: The Receiver Operating Characteristic (ROC) curve plots the true positive rate against the false positive rate. The Area Under the Curve (AUC) quantifies the overall performance of the model across all possible classification thresholds.
• 🔑 Cross-Validation: A technique to assess how well the results of a statistical analysis will generalize to an independent data set. Common methods include k-fold cross-validation.

🧪 Practice Quiz

1. Which metric measures the proportion of correctly classified instances?
   1. A. Precision
   2. B. Recall
   3. C. Accuracy
   4. D. F1-Score
2. What does Precision measure?
   1. A. How many actual positives were correctly predicted.
   2. B. How many instances predicted as positive are actually positive.
   3. C. The overall correctness of the model.
   4. D. The balance between positive and negative predictions.
3. Which metric is the harmonic mean of precision and recall?
   1. A. Accuracy
   2. B. Recall
   3. C. F1-Score
   4. D. AUC
4. What does a Confusion Matrix visualize?
   1. A. The relationship between features.
   2. B. The model's architecture.
   3. C. The model's performance by showing True Positives, True Negatives, False Positives and False Negatives.
   4. D. The distribution of data.
5. What does the AUC (Area Under the Curve) represent in the context of an ROC curve?
   1. A. The optimal threshold for classification.
   2. B. The model's training time.
   3. C. The overall performance of the model across all possible classification thresholds.
   4. D. The model's complexity.
6. What is the purpose of cross-validation?
   1. A. To improve the model's accuracy on the training data.
   2. B. To assess how well the results of a statistical analysis will generalize to an independent data set.
   3. C. To reduce the model's complexity.
   4. D. To speed up the training process.
7. Which of the following metrics is most sensitive to imbalanced datasets?
   1. A. F1-Score
   2. B. Accuracy
   3. C. Recall
   4. D. Precision