Selective Risk From Accepted Errors Tool

Description

Return selective error rate among accepted predictions.

Classifier metrics count particular outcomes on a declared population and positive-label convention. Precision, recall and accepted-only measures use different denominators. A reporting value at an empty denominator is a policy choice, while one cell, derivative or contribution does not constitute a complete performance evaluation.

Return selective error rate among accepted predictions.

When to use this tool

Use Selective Risk From Accepted Errors when checking the stated confusion-count, metric contribution or abstention-denominator convention.

How to use the tool and how it works

Enter accepted, errors. Use the units and conventions below.
Run the tool. Correct any format, range, or cross-field input error before interpreting the output.
Read risk. Use the output convention below.

Return selective error rate among accepted predictions. Require whole accepted >= 1 and errors in [0,accepted]; risk = errors/accepted.^[1]

Input field	Meaning and units	Accepted input
accepted	Accepted	Required. Safe integer; minimum 1.
errors	Errors	Required. Safe integer; minimum 0.

Output field	Interpretation	Returned shape
risk	Risk	Required. Finite number.

json

{
  "input": {
    "accepted": 80,
    "errors": 8
  },
  "output": {
    "risk": 0.1
  }
}

Limitations and assumptions

Abstentions are outside this denominator; no overall coverage, uncertainty or population risk is estimated.
Inputs must satisfy both the field types and the cross-field conditions above. Invalid input causes an error; a successful false flag or documented placeholder is a different result.
Numeric calculations use binary floating point unless exact decimal-integer arithmetic is explicitly stated. No hidden measurement tolerance or decimal rounding is added; nonfinite or out-of-contract results are rejected. Numeric signed-zero results are exported as zero.

Alternative or Complementary methods

Evaluate the complete confusion table, relevant class supports and abstention coverage together; use suitable uncertainty and validation methods for conclusions about predictive performance.

References

Calculation and input contract: classification/selective-risk-from-accepted-errors — tool.io

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