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Found 5 papers, 4 papers with code
Explanation-by-Example Based on Item Response Theory
Intelligent systems that use Machine Learning classification algorithms are increasingly common in everyday society.
Label noise detection under the Noise at Random model with ensemble filters
This work investigates the performance of ensemble noise detection under two different noise models: the Noisy at Random (NAR), in which the probability of label noise depends on the instance class, in comparison to the Noisy Completely at Random model, in which the probability of label noise is entirely independent.
Data vs classifiers, who wins?
The experiments covered by Machine Learning (ML) must consider two important aspects to assess the performance of a model: datasets and algorithms.
Decoding machine learning benchmarks
This work applied IRT to explore the well-known OpenML-CC18 benchmark to identify how suitable it is on the evaluation of classifiers.
$β^3$-IRT: A New Item Response Model and its Applications
Item Response Theory (IRT) aims to assess latent abilities of respondents based on the correctness of their answers in aptitude test items with different difficulty levels.
