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In-sample evaluation is an approach to algorithm evaluation whereby the learned model is evaluated on the data from which it was learned. This provides a biased estimate of learning performance, in contrast to holdout evaluation.
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(2017). In-Sample Evaluation. In: Sammut, C., Webb, G.I. (eds) Encyclopedia of Machine Learning and Data Mining. Springer, Boston, MA. https://doi.org/10.1007/978-1-4899-7687-1_405
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DOI: https://doi.org/10.1007/978-1-4899-7687-1_405
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Publisher Name: Springer, Boston, MA
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