Research Article
Systematic Framework to Predict Early-Stage Liver Carcinoma Using Hybrid of Feature Selection Techniques and Regression Techniques
Table 10
Quasi-Constant 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 | 0.5534 | 0.2594 | 0.126 | 0.1694 | Ridge Regression | 0.5534 | 0.2612 | 0.126 | 0.1692 | 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 | 0.0318 | 0.0051 | 0.1936 | Support Vector Regression | 0.7063 | 0.1806 | 0.1022 | 0.1782 | Multilayer Perceptron Regression | −33.3501 | −24.4743 | 1.1048 | 0.9933 | Random Forest Regression | 0.8912 | 0.4864 | 0.0622 | 0.141 |
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