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AutoEval: Are Labels Always Necessary for Classifier Accuracy Evaluation?

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    Evaluating machine learning model accuracy on unlabeled data is challenging. This study introduces Automatic model Evaluation (AutoEval) to predict classifier performance on unseen datasets using feature statistics, addressing real-world evaluation needs.

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    Area of Science:

    • Computer Science
    • Artificial Intelligence
    • Machine Learning

    Background:

    • Evaluating machine learning models on labeled datasets is standard practice.
    • Real-world scenarios often involve unlabeled test data, making traditional evaluation methods infeasible.
    • There is a need for methods to assess model performance on unlabeled data.

    Purpose of the Study:

    • To investigate and address the problem of Automatic model Evaluation (AutoEval) for classifiers on unlabeled test data.
    • To develop a method for estimating a classifier's accuracy on various unlabeled datasets.
    • To explore the relationship between distribution shift and classifier accuracy.

    Main Methods:

    • Constructed a meta-dataset comprising datasets with various transformations (e.g., rotation, background substitution).
    • Conducted correlation studies revealing a negative linear relationship between classifier accuracy and distribution shift.
    • Formulated AutoEval as a dataset-level regression problem, training regression models (e.g., neural networks) to predict accuracy from feature statistics.

    Main Results:

    • The meta-dataset demonstrated sufficient diversity for training robust regression models.
    • The developed regression models provided reasonable and promising predictions of classifier accuracy on unseen test sets.
    • Established a reliable method for estimating model performance in unlabeled data scenarios.

    Conclusions:

    • Automatic model Evaluation (AutoEval) offers a viable solution for assessing classifier performance on unlabeled data.
    • The findings highlight the potential of using feature statistics and regression models for accuracy prediction.
    • Further research can explore application scopes, limitations, and future directions of AutoEval.