auditor: an R Package for Model-Agnostic Visual Validation and Diagnostics
Abstract
Domain fit: Niche / domain-specific · No strong AI-core implementation/artifact signals were detected from current providers.
Machine learning models have spread to almost every area of life. They are successfully applied in biology, medicine, finance, physics, and other fields. With modern software it is easy to train even a~complex model that fits the training data and results in high accuracy on the test set. The problem arises when models fail confronted with real-world data. This paper describes methodology and tools for model-agnostic audit. Introduced techniques facilitate assessing and comparing the goodness of fit and performance of models. In~addition, they may be used for the analysis of the similarity of residuals and for identification of~outliers and influential observations. The examination is carried out by diagnostic scores and visual verification. Presented methods were implemented in the auditor package for R. Due to flexible and~consistent grammar, it is simple to validate models of any classes.
Results and benchmarks
Machine learning models have spread to almost every area of life.
Benchmark evidence is limited
Evidence graph: 2 refs, 1 links.
Utility signals: depth 45/100, grounding 58/100, status medium.
Implementation
No direct implementation yet
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Time to first repro: a few days
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Reproduction readiness
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Hardware requirements
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Validation caveat
Hugging Face artifacts
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Models
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Research context
6
Citations
65
References
Tasks
Computer science, Outlier, Audit, Identification (biology), Set (abstract data type), Goodness of fit, Similarity (geometry), Data mining
Methods
None detected
Domains
Artificial intelligence, Machine learning
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