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

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