Configure Metric Comparison
Use matching comma-separated entries for labels, model values, and weights.
Formula Used
Each formula converts metric values into comparable decision signals.
The area formula connects adjacent normalized radii around equal angles. Scores remain comparable even when the chart displays raw values.
How to Use This Calculator
- Enter matching metric labels separated by commas.
- Add one value per metric for every active model.
- Set weights matching the labels count and order.
- Choose scale boundaries covering every submitted value.
- Select normalized or raw chart display behavior.
- Choose axis sorting, precision, fill, and legend options.
- Press the calculation button to display results above.
- Review rankings, balance, coverage, strengths, and weaknesses.
- Export the summary or download the chart image.
Understanding Metric Radar Comparisons
Metric Radar Charts Explained
Metric radar charts compare several evaluation measures across shared axes. Each spoke represents one metric with a consistent scale. Connected points reveal overall model performance patterns very quickly.
Why Balanced Evaluation Matters
Single metrics often hide important model weaknesses or tradeoffs. Accuracy can appear deceptively strong while minority recall remains poor. Radar charts expose these imbalances through visible shape differences.
Core Calculation Approach
The calculator normalizes every metric before plotting comparable values. Min-max scaling reliably maps values between zero and one. Normalized scaling maps values onto the selected percentage range.
Weighted Composite Scoring
Weights let important metrics influence the final score more. Each normalized metric is then multiplied by its assigned weight. Weighted results are divided by the total active weight.
Understanding Chart Area
A larger polygon usually suggests broader performance across metrics. Area alone cannot conclusively prove one model is operationally better. Critical metrics may deserve priority despite a smaller polygon.
Reading Balance Scores
Balance measures how evenly a model performs across metrics. Lower variation generally creates a higher balance score. Balanced models can behave more consistently across changing conditions.
Comparing Multiple Models
Overlayed polygons make competing model tradeoffs easier to inspect. One model may dominate precision while another leads recall. Decision makers can match those strengths to project risks.
Choosing Useful Metrics
Select metrics that directly reflect real deployment goals and consequences. Classification tasks often include precision, recall, F1, and specificity. Regression comparisons may use MAE, RMSE, R-squared, and MAPE.
Using Weights Carefully
Weights should represent business costs, safety needs, or priorities. Avoid changing weights merely to favor a preferred model. Document every weighting choice for transparent future reviews.
Interpreting Strong and Weak Axes
The longest spoke identifies a model's strongest normalized metric. The shortest spoke highlights its clearest evaluation weakness. Investigate weak axes before approving production deployment decisions.
Normalization Limitations
Normalization improves comparison but can hide original measurement units. Always review original raw values beside normalized chart values. Consistent scale boundaries prevent misleading visual differences between models.
Practical Review Workflow
Start with default weights and inspect every polygon shape. Then carefully adjust weights using agreed operational priorities. Recheck rankings after each justified weighting change.
Common Mistakes
Do not mix percentages, ratios, and counts without normalization. Avoid crowded charts containing too many similar metrics. Five to ten meaningful axes usually remain readable.
Model Governance Benefits
Radar charts support structured model review meetings and documentation. Teams can discuss visible performance compromises using shared evidence. Saved settings also improve repeatability during later audits.
Final Evaluation Guidance
Use radar charts as decision support, not final proof. Combine them with confusion matrices and validation reports. Deployment decisions should include fairness, stability, monitoring, and review plans.
Frequently Asked Questions
What does a metric radar chart show?
It plots several evaluation metrics on radial axes. Each model forms a polygon across those axes. Shape differences reveal strengths, weaknesses, and tradeoffs.
How many metrics should I enter?
Use at least three metrics because polygons need three axes. The calculator accepts up to twelve metrics. Five through ten axes usually provide clearer comparisons.
Why should values be normalized?
Normalization places unlike measurement ranges on one comparable scale. It prevents large numeric units from dominating smaller units. Raw values remain useful when every metric already shares boundaries.
How do metric weights affect rankings?
Higher weights increase a metric's contribution to composite scoring. A zero weight removes that metric from ranking calculations. The chart still displays every entered metric.
What does the balance score mean?
The balance score estimates consistency across normalized metrics. Lower variation produces a higher score. It does not replace task-specific thresholds or risk reviews.
What is radar area coverage?
Area coverage compares the model polygon with the maximum polygon. Higher coverage suggests broader performance across axes. However, important weak metrics still require direct inspection.
Can I compare regression models?
Yes, but lower-is-better errors need transformation before entry. Convert errors into consistent utility scores or reversed normalized values. Document that transformation for accurate interpretation.
Why might the largest polygon lose?
The weighted score emphasizes selected priorities rather than visual area. A smaller polygon can excel on heavily weighted metrics. Always compare scores with operational requirements.
Can I export the generated results?
Yes, the result section includes a CSV download button. The chart toolbar can export a high-resolution image. These exports support reports and model reviews.