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.
Python Tutorial
How to create a calibration curve in Python
Use the full tutorial for implementation details, troubleshooting, and chart variations in matplotlib, seaborn, and plotly.
Complete Guide to Scientific Data VisualizationExample 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
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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
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-curve-ml.
numpy
Useful in specialized workflows that complement core Python plotting libraries for calibration-curve-ml analysis tasks.
sklearn
Useful in specialized workflows that complement core Python plotting libraries for calibration-curve-ml analysis tasks.
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