Calibration Plot
Chart overview
Calibration plots compare a model predicted probability of an event against the actual observed proportion in binned groups of patients, revealing whether a model is overconfident, underconfident, or well-calibrated.
Key points
- Clinical epidemiologists and biostatisticians use calibration plots alongside Hosmer-Lemeshow tests and calibration slope metrics when reporting clinical prediction models following TRIPOD guidelines.
- The diagonal ideal calibration line serves as the reference against which model performance is evaluated.
Python Tutorial
How to create a calibration plot in Python
Use the full tutorial for implementation details, troubleshooting, and chart variations in matplotlib, seaborn, and plotly.
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"Create a calibration plot from my data. Plot mean predicted probability on the x-axis and observed event rate on the y-axis with error bars for each bin. Draw the perfect calibration diagonal as a dashed line. Overlay a LOESS regression calibration curve. Include a histogram of predicted probabilities along the bottom x-axis. Use journal formatting with Arial font and no top or right spines."
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Python Code Example
Console Output
Figure saved: plotivy-calibration-plot.png
Common Use Cases
- 1Validating clinical prediction models for mortality, readmission, or disease probability
- 2Reporting model calibration in TRIPOD-compliant prognostic model development studies
- 3Comparing pre- and post-recalibration performance of risk scores applied to new populations
- 4Assessing machine learning classifier reliability in imbalanced clinical datasets
Pro Tips
Use at least 10 equally sized bins of predicted probability to balance resolution and stability
Add Wilson score confidence intervals to observed proportions in each bin to reflect uncertainty
Include the Brier score and calibration slope in a text box inset for a complete model summary
Plot the distribution of predicted probabilities as a rug or histogram to detect extreme predictions
Long-tail keyword opportunities
High-intent chart variations
Library comparison for this chart
matplotlib
Best when you need full control over axis formatting, annotation placement, and journal-specific styling for calibration-plot.
numpy
Useful in specialized workflows that complement core Python plotting libraries for calibration-plot analysis tasks.
scipy
Useful in specialized workflows that complement core Python plotting libraries for calibration-plot analysis tasks.
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.