Research Article
Systematic Framework to Predict Early-Stage Liver Carcinoma Using Hybrid of Feature Selection Techniques and Regression Techniques
Table 2
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 Regression | 0.9993 | 0.0587 | 0.0051 | 0.1909 | Support Vector Regression | 0.5382 | 0.3475 | 0.1281 | 0.159 | Multilayer Perceptron Regression | 0.2222 | 0.0461 | 0.1662 | 0.1922 | Random Forest Regression | 0.8921 | 0.4851 | 0.0619 | 0.1412 |
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