How do higher R2 values indicate better performance in corrosion prediction models?
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Higher R2 values in corrosion prediction models indicate better performance by showcasing the degree of variance in the data that is explained by the model. A study on corrosion inhibitors highlights that a reliable prediction model is determined by high R values and low Mean Squared Error (MSE), indicating a robust prediction capability. Similarly, another research emphasizes achieving a coefficient of determination (R2) above 0.99 in artificial neural network models, signifying a strong agreement between simulations and experimental data. Furthermore, a hybrid model developed using machine learning algorithms demonstrates an accuracy of 91.46% in predicting corrosion rates, showcasing the effectiveness of higher R2 values in enhancing predictive capabilities. Overall, higher R2 values signify a better fit of the model to the data, indicating superior performance in corrosion prediction models.
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| Papers (5) | Insight |
|---|---|
3 Citations | Higher R2 values in corrosion prediction models, such as FCM-clustered ANFIS, indicate stronger correlation between predicted and actual corrosion rates, reflecting better model performance and accuracy in corrosion inhibition prediction. |
| Higher R2 values in corrosion prediction models indicate better performance as they signify a stronger correlation between the predicted and actual corrosion rates, with 91.46% accuracy in the Hybrid model. | |
01 Jul 2022 | Higher R2 values indicate better performance in corrosion prediction models as they signify a stronger correlation between predicted and actual corrosion rates, with 91.46% accuracy achieved in the Hybrid model. |
| Higher R2 values, above 0.99 in this study, indicate strong agreement between predicted and actual data in corrosion prediction models, reflecting accurate simulations of electrochemical behavior at low frequencies. | |
22 Oct 2022 | Higher R2 values in corrosion prediction models indicate better performance as they signify a stronger correlation between predicted and actual values, enhancing the reliability of the neural network model. |
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