Model Performance: Accuracy, Precision, and Recall Quiz

📚 Quick Study Guide

• 📊 Accuracy: The ratio of correctly predicted observations to the total observations. Formula: $\text{Accuracy} = \frac{\text{True Positives + True Negatives}}{\text{Total Observations}}$
• 🎯 Precision: The ratio of correctly predicted positive observations to the total predicted positive observations. It answers the question: 'Of all the instances predicted as positive, how many are actually positive?' Formula: $\text{Precision} = \frac{\text{True Positives}}{\text{True Positives + False Positives}}$
• Recall: The ratio of correctly predicted positive observations to all observations in the actual class. It answers the question: 'Of all the instances that are actually positive, how many are correctly predicted?' Formula: $\text{Recall} = \frac{\text{True Positives}}{\text{True Positives + False Negatives}}$
• ✨ F1-Score: The harmonic mean of precision and recall, providing a balanced measure. Formula: $\text{F1-Score} = 2 \times \frac{\text{Precision} \times \text{Recall}}{\text{Precision + Recall}}$
• 📉 Trade-off: Precision and recall often have an inverse relationship. Increasing one can decrease the other.
• 🔑 Confusion Matrix: A table that describes the performance of a classification model. It includes True Positives, True Negatives, False Positives, and False Negatives.

🧪 Practice Quiz

1. Question 1: What does accuracy measure in model performance?
   1. A. The correctness of positive predictions.
   2. B. The correctness of negative predictions.
   3. C. The overall correctness of predictions.
   4. D. The balance between precision and recall.
2. Question 2: Which metric answers the question: 'Of all the instances predicted as positive, how many are actually positive?'
   1. A. Recall
   2. B. Accuracy
   3. C. Precision
   4. D. F1-Score
3. Question 3: What is the formula for calculating recall?
   1. A. $\frac{\text{True Positives}}{\text{True Positives + False Positives}}$
   2. B. $\frac{\text{True Positives + True Negatives}}{\text{Total Observations}}$
   3. C. $\frac{\text{True Positives}}{\text{True Positives + False Negatives}}$
   4. D. $2 \times \frac{\text{Precision} \times \text{Recall}}{\text{Precision + Recall}}$
4. Question 4: What is the F1-Score a harmonic mean of?
   1. A. Accuracy and Recall
   2. B. Precision and Accuracy
   3. C. Precision and Specificity
   4. D. Precision and Recall
5. Question 5: What does a confusion matrix primarily help to visualize?
   1. A. Data distribution
   2. B. Model performance
   3. C. Feature importance
   4. D. Algorithm complexity
6. Question 6: In a classification model, if you want to minimize false positives, which metric should you focus on improving?
   1. A. Recall
   2. B. Accuracy
   3. C. Precision
   4. D. F1-Score
7. Question 7: What does it mean if a model has high recall but low precision?
   1. A. It has few false negatives.
   2. B. It has few false positives.
   3. C. It is highly accurate.
   4. D. It is perfectly balanced.