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reliability diagram

A reliability diagram (also called a calibration curve) is a plot used to evaluate how well-calibrated a probabilistic classifier is. The x-axis shows bins of predicted probabilities (e.g., 0.0–0.1, 0.1–0.2, etc.), and the y-axis shows the…

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Definition

A reliability diagram (also called a calibration curve) is a plot used to evaluate how well-calibrated a probabilistic classifier is. The x-axis shows bins of predicted probabilities (e.g., 0.0–0.1, 0.1–0.2, etc.), and the y-axis shows the empirical fraction of positives observed in each bin. A perfectly calibrated model follows the diagonal identity line; deviations indicate miscalibration — systematic overprediction (curve below diagonal) or underprediction (curve above diagonal). The diagram enables practitioners to diagnose and correct calibration errors using methods such as isotonic regression or Platt scaling. [formal: reliability diagram | substrate: mind | horizon: a moment | explicit: yes | epoch: 0.01]

Why it is in scope

A reliability diagram is a human-made visualization tool used in machine learning and statistics to assess the calibration of probabilistic predictions. It plots predicted probability bins against the observed frequency of the positive class, producing a curve that reveals systematic overconfident or underconfident calibration errors. Built to persist through standard plotting libraries and model evaluation frameworks.

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Relations from this entry

  • cmrvfy7ya011u2ceilq543hu4INSTANCE_OF →

    TESTED INSTANCE_OF: A reliability diagram IS a specific kind of plot — it plots predicted probabilities against observed frequencies on a Cartesian coordinate system with axes. A competent speaker would call it 'a plot'.

  • cmsetf6qd05wb3vv34zrnqmnkDEPENDS_ON →

    A reliability diagram is a visualization that assesses probability forecasts — it takes forecasts as input and plots calibrated reliability. Remove probability forecasts and there is nothing to diagram. The diagram's entire purpose is to operate on forecasts.

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Created
Aug 4, 2026, 5:58 PM UTC
Content hash
7d2f1ea17cf5cf468a3b39c97ba501b9825147426cdbee8ebd12f8cdf124c9d1

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