In regression applications, there is no single algorithm which performs well with all data since the performance of an algorithm depends on the dataset used. In practice, different algorithms / approaches are tried, and the best one is selected in each application. It is meaningful to ask whether there is a different way instead of running such tedious experiments. In meta learning studies, one investigates clues for the performance of an algorithm over a dataset using several features of the dataset. In this context, it is important to estimate which dataset features (meta features) are most significant for the performance of the algorithm.

In the literature, meta learning studies mostly specialize to classification problems. In this study, meta regression problems are comprehensively studied on 3 big dataset collections (totally 181 datasets). New and existent meta features (about 300) are used. The relationships between the datasets and the algorithms are investigated. Several relations are found between meta features and related performances. The created meta datasets are made available to interested researchers.

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