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The model selection procedure is usually a single-criterion decision makingin which we select the model that maximizes a specific metric in a specificset, such as the Validation set performance. We claim this is very naive andcan perform poor selections of over-fitted models due to the over-searchingphenomenon, which over-estimates the performance on that specific set.Futhermore, real world data contains noise that should not be ignored by themodel selection procedure and must be taken into account when performing modelselection. Also, we have defined four theoretical optimality conditions that wecan pursue to better select the models and analyze them by using amulti-criteria decision-making algorithm (TOPSIS) that considers proxies to theoptimality conditions to select reasonable models.

Have we been Naive to Select Machine Learning Models? Noisy Data are here to Stay!