Performance measures of algorithms
Performance measures | Algorithm | |||||
---|---|---|---|---|---|---|
SVM | DT | RF | LR | XGBoost | KNN | |
R-square | 0.10 | 0.57 | 0.77 | 0.68 | 0.79 | 0.31 |
Adjusted R-square | 0.09 | 0.57 | 0.77 | 0.68 | 0.78 | 0.30 |
Mean square error (MSE) | 149,194,459.22 | 58,612,767.52 | 30,736,317.10 | 43,614,936.74 | 29,099,812.49 | 94,609,424.94 |
Root MSE (RMSE) | 12,214.52 | 7,595.89 | 5,544.03 | 6,604.16 | 5,394.42 | 9,726.74 |
Mean absolute error (MAE) | 8,188.16 | 5,170.15 | 4,066.94 | 5,072.64 | 3,870.03 | 6,769.37 |
Mean absolute percentage error (MAPE) | 1.01 | 0.72 | 0.66 | 0.71 | 0.63 | 0.98 |
AA: Conceptualization, Data curation, Formal analysis, Methodology, Software, Writing—original draft, Writing—review & editing. VRP: Supervision, Validation, Writing—review & editing. All authors read and approved the submitted version.
The authors declare that they have no conflicts of interest.
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The datasets analyzed for this study can be found in Kaggle by Sumit Kumar Shukla (https://www.kaggle.com/datasets/thedevastator/insurance-claim-analysis-demographic-and-health).
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© The Author(s) 2024.