Although fundamental to the advancement of Machine Learning, the classic evaluation metrics extracted from the confusion matrix, such as precision and F1, are limited. Such metrics only offer a quantitative view of the models' performance, without considering the complexity of the data or the quality of the hit. To overcome these limitations, recent research has introduced the use of psychometric metrics such as Item Response Theory (IRT), which allows an assessment at the level of latent characteristics of instances. This work investigates how IRT concepts can enrich a confusion matrix in order to identify which model is the most appropriate among options with similar performance. In the study carried out, IRT does not replace, but complements classical metrics by offering a new layer of evaluation and observation of the fine behavior of models in specific instances. It was also observed that there is 97% confidence that the score from the IRT has different contributions from 66% of the classical metrics analyzed.

本研究解决了传统混淆矩阵评价指标（如精度和F1分数）只提供定量分析而忽视数据复杂性和结果质量的局限性。创新性地引入了心理测量指标，如项目反应理论（IRT），为混淆矩阵增添了评估隐性特征的新层面，显著提高了举例分析的能力。研究发现，IRT评分与66%经典指标的贡献存在97%的信心，表明这一方法的潜在影响力。

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