Research Article
Systematic Framework to Predict Early-Stage Liver Carcinoma Using Hybrid of Feature Selection Techniques and Regression Techniques
Table 7
L2 Regularization-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 | 0.2606 | 0.126 | 0.1692 | LASSO Regression | 0.3327 | 0.3528 | 0.154 | 0.1583 | Elastic Net Regression | 0.3954 | 0.4147 | 0.1466 | 0.1506 | Decision Tree | 0.9993 | 0.0103 | 0.0051 | 0.1958 | Support Vector Regression | 0.5382 | 0.3475 | 0.1281 | 0.159 | Multilayer Perceptron Regression | 0.4026 | 0.3289 | 0.1457 | 0.1612 | Random Forest Regression | 0.8917 | 0.4798 | 0.062 | 0.1419 |
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