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1.Evaluation Metrics for Classification[Original Blog]
1. Accuracy:
- Accuracy is perhaps the most straightforward metric. It measures the proportion of correctly predicted instances out of the total instances. Mathematically, it's defined as:
\[ \text{Accuracy} = \frac{\text{Correct Predictions}}{\text{Total Instances}} \]
- Example: Suppose we have a binary classification problem (e.g., spam detection). If our model correctly classifies 900 out of 1000 emails, the accuracy is 90%.
2. Precision and Recall:
- Precision (also known as positive predictive value) focuses on the proportion of true positive predictions among all positive predictions:
\[ \text{Precision} = \frac{\text{True Positives}}{\text{True Positives + False Positives}} \]
- Recall (also known as sensitivity or true positive rate) emphasizes the proportion of true positive predictions among all actual positive instances:
\[ \text{Recall} = \frac{\text{True Positives}}{\text{True Positives + False Negatives}} \]
- Example: In medical diagnosis, high recall is crucial to avoid missing actual cases (e.g., detecting diseases).
3. F1-Score:
- The F1-score balances precision and recall. It's the harmonic mean of the two:
\[ F1 = \frac{2 \cdot \text{Precision} \cdot \text{Recall}}{\text{Precision} + \text{Recall}} \]
- It's useful when precision and recall have different priorities.
4. Receiver Operating Characteristic (ROC) Curve:
- The ROC curve visualizes the trade-off between true positive rate (recall) and false positive rate (1-specificity) across different probability thresholds.
- The area under the ROC curve (AUC-ROC) quantifies the overall performance of the model. AUC values close to 1 indicate excellent performance.
- Example: In fraud detection, we want high true positive rates while keeping false positives low.
5. Area Under the precision-Recall curve (AUC-PR):
- Similar to the ROC curve, the precision-recall curve plots precision against recall.
- AUC-PR summarizes the model's performance across different recall levels.
- It's useful when dealing with imbalanced datasets.
6. Confusion Matrix:
- The confusion matrix provides a detailed breakdown of true positives, true negatives, false positives, and false negatives.
- From the confusion matrix, we can calculate various metrics like accuracy, precision, recall, and F1-score.
7. Specificity (True Negative Rate):
- Specificity measures the proportion of true negative predictions among all actual negative instances:
\[ \text{Specificity} = \frac{\text{True Negatives}}{ ext{True Negatives + False Positives}} \]
- It complements recall and is essential in scenarios where avoiding false alarms is critical.
Remember that the choice of evaluation metric depends on the problem context, business goals, and the relative importance of different types of errors. By understanding these metrics, we can make informed decisions about our classification models and continuously improve their performance.
Evaluation Metrics for Classification - Classification Understanding Classification Algorithms: A Comprehensive Guide