Calibration Curve
Chart overview
A calibration curve plots the mean predicted probability per bin against the actual fraction of positive outcomes, showing whether a model is over-confident, under-confident, or well-calibrated.
Key points
- It is an essential diagnostic in clinical risk prediction, weather forecasting, and any domain where probability estimates drive decisions.
- A perfectly calibrated model follows the diagonal, and deviations directly quantify the degree of miscalibration.
Example Visualization
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"Create a calibration curve from my predicted probabilities and true labels. Plot the reliability diagram with the perfect calibration diagonal as a dashed reference, add a histogram of predicted probability distribution below the main plot, and compare multiple models if provided."
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Python Code Example
Console Output
Figure saved: plotivy-calibration-curve-ml.png
Common Use Cases
- 1Validating that a clinical risk score gives accurate event probabilities
- 2Comparing isotonic regression vs. Platt scaling recalibration methods
- 3Checking calibration of ensemble models before deploying to production
- 4Evaluating weather forecast probability skill across precipitation bins
Pro Tips
Use at least 10 equally spaced bins and report the number of samples per bin
Add confidence intervals around each calibration point using bootstrap resampling
Report the Expected Calibration Error (ECE) as a scalar summary in the title or caption
Plot the histogram on a secondary y-axis below the diagram to show sample density per bin
Scientific Chart Selection Cheat Sheet
Not sure whether to use a Violin Plot, Box Plot, or Ridge Plot? Download our single-page reference mapping the most-used scientific chart types, exactly when to use them, and the core Matplotlib/Seaborn functions.