MELGJun 9, 2023

Null/No Information Rate (NIR): a statistical test to assess if a classification accuracy is significant for a given problem

arXiv:2306.06140v17 citationsh-index: 32
Originality Synthesis-oriented
AI Analysis

This addresses the need for researchers, especially in biomedical fields, to validate classification accuracy beyond simple thresholds, though it appears incremental as it formalizes an existing statistical concept.

The paper tackles the problem of determining whether a classification system's accuracy is statistically significant for a given problem, introducing the Null/No Information Rate (NIR) test to assess this with confidence.

In many research contexts, especially in the biomedical field, after studying and developing a classification system a natural question arises: "Is this accuracy enough high?", or better, "Can we say, with a statistically significant confidence, that our classification system is able to solve the problem"? To answer to this question, we can use the statistical test described in this paper, which is referred in some cases as NIR (No Information Rate or Null Information Rate).

Foundations

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