Calculator Inputs and Display Options
How to Use This Calculator
- Enter confidence values using one consistent scale.
- Add matching actual and predicted label sequences.
- Choose thresholds, bins, filters, sorting, and display options.
- Submit the form to generate metrics and interactive plots.
- Review confident errors before trusting deployment decisions.
Use representative validation data for meaningful conclusions. Compare several thresholds before selecting an operating policy. Recheck results after every important model or data change.
Formula Used
Accuracy = Correct Predictions / Total Predictions
Coverage = Accepted Predictions / Total PredictionsSelective Accuracy = Correct Accepted Predictions / Accepted Predictions
ECE = Σ (nᵦ / N) × |Accuracyᵦ − Confidenceᵦ|
MCE = max |Accuracyᵦ − Confidenceᵦ|
Brier = (1 / N) × Σ (pᵢ − yᵢ)²
Log Loss = −(1 / N) × Σ[yᵢ log(pᵢ) + (1 − yᵢ) log(1 − pᵢ)]
Here, yᵢ equals one for correct predictions. It equals zero for incorrect predictions. Each pᵢ is the reported top-class confidence.
Understanding Prediction Confidence
Why Confidence Plots Matter
Prediction confidence shows model support for each output. High confidence can indicate certainty, but it never guarantees correctness. Confidence plots expose mistakes that simple accuracy scores often hide.
Core Interpretation
Each point represents a prediction and reported confidence. Correct and incorrect outcomes can use separate markers for comparison. Large error clusters reveal regions where model trust becomes risky.
Threshold Analysis
A decision threshold converts confidence into an action. Raising thresholds usually reduces accepted predictions and increases abstentions. Lower thresholds improve coverage but may admit more incorrect predictions.
Calibration Insights
Calibration compares confidence with observed correctness across bins. Perfect calibration means confidence closely matches the actual success rate. Large gaps suggest probabilities require recalibration before sensitive deployment.
Reliability Bins
Bins group confidence values for stable summaries. More bins reveal detail but create noisier estimates with limited samples. Fewer bins provide stability while hiding smaller local confidence problems.
Expected Calibration Error
Expected calibration error summarizes weighted gaps across bins. Smaller values usually indicate better agreement between confidence and accuracy. However, one number cannot reveal every harmful regional pattern.
Maximum Calibration Error
Maximum calibration error reports largest bin difference. It highlights the worst confidence region instead of overall average behavior. This metric helps teams locate critical reliability failures quickly.
Confidence Distribution
Histograms show whether predictions concentrate near certainty or ambiguity. A high-confidence peak may impress but still mislead. Always compare distribution shape with error locations and calibration gaps.
Sorting and Smoothing
Sorting confidence clarifies sample progression and outcomes. Moving averages reduce visual noise while preserving broad confidence trends. Excessive smoothing can conceal sudden failures that deserve investigation.
Class-Based Review
Filtering by class uncovers differences among categories. Some classes remain reliable while others produce confident mistakes frequently. Class review supports targeted data collection and model improvement.
Operational Use
Teams can set review zones for risky predictions. Abstention rules send low-confidence cases toward human assessment. These policies balance automation speed with safer decision control.
Data Quality Effects
Noisy labels and shifts distort confidence behavior. Duplicated samples may inflate apparent certainty during evaluation. Use independent validation data that reflects realistic deployment conditions.
Model Comparison
Compare models using identical samples, bins, thresholds, rules. A model with lower accuracy may provide better calibrated probabilities. Choose metrics matching business costs rather than one headline score.
Responsible Interpretation
Confidence measures belief, not truth or suitability. Sensitive decisions require fairness checks, monitoring, and human oversight. Document thresholds and limitations before releasing automated prediction workflows.
Practical Review Routine
Start with sample points, then inspect bins and calibration metrics. Review confident errors before studying uncertain correct predictions. Repeat this process whenever data, models, or policies change.
Frequently Asked Questions
What is prediction confidence?
Prediction confidence is the model's reported certainty for one output. It usually ranges from zero to one. Higher values do not guarantee correct predictions.
Why separate correct and incorrect predictions?
Separate groups reveal where confidence aligns with actual performance. Confident errors deserve immediate attention. Uncertain correct predictions may indicate conservative scoring.
What does the threshold control?
The threshold defines which predictions receive automatic acceptance. Lower values increase coverage. Higher values usually increase abstention and human review.
What is expected calibration error?
Expected calibration error averages confidence gaps across populated bins. Each gap receives sample-based weighting. Smaller values generally indicate better probability alignment.
How many calibration bins should I use?
Ten bins provide a practical starting point. Small datasets often need fewer bins. Large datasets can support more detailed calibration views.
What does the Brier score measure?
The score measures squared error between confidence and correctness. Lower values indicate better agreement. This calculator treats correctness as a binary outcome.
Why can high confidence still be wrong?
Models can learn biased patterns or encounter unfamiliar data. Confidence reflects model belief only. It does not verify factual truth.
Should confidence values be percentages?
Either scale works when selected correctly. Probability values range from zero to one. Percentage values range from zero to one hundred.
Can this calculator compare models?
Run each model with identical samples and settings. Compare calibration, errors, coverage, and selective accuracy. Keep evaluation conditions consistent for fair conclusions.