विधियों की तुलना करें
चुनी हुई विधियों की आमने-सामने समीक्षा करें; भिन्नता वाली पंक्तियाँ रेखांकित हैं।
| आर-स्क्वेयर्ड (आर²)× | मूल माध्य वर्ग त्रुटि (Root Mean Squared Error - RMSE)× | |
|---|---|---|
| क्षेत्र | मॉडल मूल्यांकन | मॉडल मूल्यांकन |
| परिवार | MCDM | MCDM |
| उद्भव वर्ष≠ | 1896 | 1809 |
| प्रवर्तक≠ | Karl Pearson | Carl Friedrich Gauss |
| प्रकार≠ | Goodness-of-fit metric | Distance-based evaluation metric |
| मौलिक स्रोत≠ | Pearson, K. (1896). Mathematical contributions to the theory of evolution. Philosophical Transactions of the Royal Society A, 187, 253-318. link ↗ | Gauss, C. F. (1809). Theoria Motus Corporum Coelestium in Sectionibus Conicis Solem Ambientium. Hamburg: Perthes and Besser. link ↗ |
| उपनाम | R², coefficient of determination, r2 score | RMSE, RMS error, quadratic mean error |
| संबंधित≠ | 5 | 4 |
| सारांश≠ | 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. | Root Mean Squared Error is a widely used metric that measures the average magnitude of prediction errors in regression models. Originating from Carl Friedrich Gauss's work on least-squares estimation (1809), RMSE quantifies how far predictions deviate from observed values by averaging the squared differences and taking the square root. |
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