A domain-region based evaluation of ML performance robustness to covariate shift

• Most machine learning methods assume that the input data distribution is the same in the training and testing phases.However, in practice, this stationarity is usually not met and the distribution of inputs differs, leading to unexpectedperformance of the learned model in deployment. The issue in which the training and test data inputs follow differentprobability distributions while the input–output relationship remains unchanged is referred to as covariate shift. In thispaper, the performance of conventional machine learning models was experimentally evaluated in the presence of covariateshift. Furthermore, a region-based evaluation was performed by decomposing the domain of probability density function ofthe input data to assess the classifier’s performance per domain region. Distributional changes were simulated in a twodimensional classification problem. Subsequently, a higher four-dimensional experiments were conducted. Based on theexperimental analysis, the Random Forests algorithm is the most robust classifier in the two-dimensional case, showing thelowest degradation rate for accuracy and F1-score metrics, with a range between 0.1% and 2.08%. Moreover, the resultsreveal that in higher-dimensional experiments, the performance of the models is predominantly influenced by the complexity of the classification function, leading to degradation rates exceeding 25% in most cases. It is also concluded that themodels exhibit high bias toward the region with high density in the input space domain of the training samples.

Ämnesord

NATURVETENSKAP  -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)

NATURAL SCIENCES  -- Computer and Information Sciences -- Computer Sciences (hsv//eng)

Nyckelord

Degradation

Function evaluation

Input output programs

Machine learning

Probability distributions

Classifier evaluation

Concept drifts

Covariate shifts

Input datas

Machine-learning

Model degradations

Performance

Region-based

Robust machine learning

Two-dimensional

Probability density function

Computer Science

Datavetenskap

Publikations- och innehållstyp

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