Confidence Level#
The long-run proportion of intervals expected to contain the true parameter.
Important
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What it is#
A model’s confidence level is the probability it attaches to its prediction — how sure it is that an instance belongs to the predicted class. It is only meaningful if it is calibrated: a model is well-calibrated when predictions made with confidence \(p\) are correct about 100p% of the time (predict 0.9 → right 90% of the time).
How it’s checked#
A reliability diagram bins predictions by confidence level and plots confidence against actual accuracy; perfect calibration lies on the diagonal. The gap is summarized by the Expected Calibration Error (ECE) — the average distance between confidence and accuracy across bins.
Why it matters#
Raw accuracy says nothing about whether the confidence is honest, yet downstream risk-based decisions (which cases to escalate, when to defer) depend on trusting the number. This is the model sense of confidence, distinct from a statistical confidence interval.
Theme: Probability & Statistics Foundations · All terminology
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Mind map — connected ideas
Overconfident · Underconfident · Classification Probability · Binary Cross-Entropy (BCE) · Risk-Based Decisions · Multiclass AUROC
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More in Probability & Statistics Foundations
Beta Distribution · Correlation · Critical Value · Cumulative Distribution Function (CDF) · Frequentist · IID (Independent and Identically Distributed) · Likelihood · Margin of Error (MoE) · Mean · Median · Normal Distribution · Outlier · Population Proportion · Probability
See also
Source article Adapted (context, re-expressed) in our own words from: Confidence Level (insightful-data-lab.com).