Complementary metrics

Complementary Metrics in Machine Learning

Complementary Metrics refer to pairs or groups of evaluation metrics that together provide a more complete and balanced understanding of a classification model’s performance. Because no single metric is perfect, especially in real-world and imbalanced datasets, these metrics are used together to highlight different strengths and weaknesses of a model.

Why Use Complementary Metrics?

Using only one metric like Accuracy can be misleading — especially when dealing with imbalanced classes. Complementary metrics help you:

• Understand different types of errors (false positives vs false negatives)
• Choose a model that fits your specific use case
• Balance trade-offs (e.g., sensitivity vs specificity)

Common Complementary Pairs

1. Precision and Recall

• Precision focuses on how many predicted positives are correct.
• Recall (or Sensitivity) focuses on how many actual positives were caught.
• Complement each other: high precision may come with low recall, and vice versa.

→ Combined using the F1 Score

2. Sensitivity and Specificity

• Sensitivity (Recall) = True Positive Rate
• Specificity = True Negative Rate
• Complement each other in binary classification tasks.

Example: In medical diagnosis,

• High Sensitivity ensures sick patients are detected.
• High Specificity ensures healthy people aren't misdiagnosed.

3. Accuracy and F1 Score

• Accuracy is good for balanced datasets.
• F1 Score is better for imbalanced data where false negatives or positives matter more.

Together, they offer a more well-rounded picture.

4. ROC and AUC

• The ROC Curve plots Sensitivity vs. 1 − Specificity.
• The AUC (Area Under the Curve) summarizes the ROC into a single score between 0 and 1.

→ These complement threshold-based metrics by offering a threshold-independent evaluation.

Real-World Example

In a **spam detection system**:

• Precision tells you how many flagged emails are actually spam (important for avoiding loss of important emails).
• Recall tells you how many spam emails the system successfully detected.
• F1 Score balances the two.

When to Use Complementary Metrics

• Your dataset is imbalanced
• You're working in high-risk domains (medicine, finance, law)
• You want a holistic view of model performance
• Model decisions have real-world consequences

Visual Tools

• Confusion Matrix: Base for calculating most metrics
• ROC Curve: Visualizes trade-offs between Sensitivity and Specificity

Related Pages

• Precision
• Recall
• Specificity
• F1 Score
• Accuracy
• ROC Curve
• Confusion Matrix

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