Σύγκριση μεθόδων
Εξετάστε τις επιλεγμένες μεθόδους δίπλα-δίπλα· οι γραμμές που διαφέρουν επισημαίνονται.
| Συντελεστής προσδιορισμού (R²)× | Μέσο Απόλυτο Σφάλμα (MAE)× | |
|---|---|---|
| Πεδίο | Αξιολόγηση Μοντέλων | Αξιολόγηση Μοντέλων |
| Οικογένεια | MCDM | MCDM |
| Έτος προέλευσης≠ | 1896 | 1799 |
| Δημιουργός≠ | Karl Pearson | Pierre-Simon Laplace |
| Τύπος≠ | Goodness-of-fit metric | Robust distance-based metric |
| Θεμελιώδης πηγή≠ | Pearson, K. (1896). Mathematical contributions to the theory of evolution. Philosophical Transactions of the Royal Society A, 187, 253-318. link ↗ | Laplace, P. S. (1799). Traité de Mécanique Céleste. Paris: J.B.M. Duprat. link ↗ |
| Εναλλακτικές ονομασίες | R², coefficient of determination, r2 score | MAE, L1 error, mean absolute deviation |
| Συναφείς≠ | 5 | 3 |
| Σύνοψη≠ | The coefficient of determination, denoted R², measures the proportion of variance in the dependent variable explained by the independent variables in a regression model. Introduced by Karl Pearson in the late 19th century, R² is one of the most widely used metrics for assessing how well a model fits observed data. | Mean Absolute Error is a robust metric that measures the average absolute magnitude of prediction errors in regression models. Dating back to Pierre-Simon Laplace's work on observational errors (1799), MAE quantifies typical prediction deviation by averaging the absolute differences between observed and predicted values. |
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