Research Article
Systematic Framework to Predict Early-Stage Liver Carcinoma Using Hybrid of Feature Selection Techniques and Regression Techniques
Table 9
Constant Feature Elimination-based Regression algorithms with accuracy and error rate during training and testing.
| Models | Accuracy (R2-Score) training (↑) | Accuracy (R2-Score) testing (↑) | MSE training (↓) | MSE testing (↓) |
| Linear Regression | 0.5519 | 0.2923 | 0.1262 | 0.1656 | Ridge Regression | 0.5519 | 0.2933 | 0.1262 | 0.1654 | LASSO Regression | 0.3511 | 0.3364 | 0.1518 | 0.1603 | Elastic Net Regression | 0.4078 | 0.4019 | 0.1451 | 0.1522 | Decision Tree | 0.9993 | −0.0004 | 0.0051 | 0.1969 | Support Vector Regression | 0.707 | 0.1797 | 0.102 | 0.1782 | Multilayer Perceptron Regression | −4303.1142 | −4577.799 | 12.3669 | 13.3172 | Random Forest Regression | 0.8907 | 0.4903 | 0.0623 | 0.1405 |
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