Research Article
Systematic Framework to Predict Early-Stage Liver Carcinoma Using Hybrid of Feature Selection Techniques and Regression Techniques
Table 13
Random Forest-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 (↓) |
| Simple Random Forest Regression | 0.8921 | 0.4851 | 0.0619 | 0.1412 | Random Forest Regression with Forward Selection (Wrapper Method) | 0.8923 | 0.5031 | 0.0618 | 0.1387 | Random Forest Regression with Backward Elimination (Wrapper Method) | 0.8923 | 0.5031 | 0.0618 | 0.1387 | Random Forest Regression with Bidirectional Elimination (Wrapper Method) | 0.8923 | 0.5031 | 0.0618 | 0.1387 | Random Forest Regression with L1 Regularization (Embedded Method) | 0.8903 | 0.4844 | 0.0624 | 0.1413 | Random Forest Regression with L2 Regularization (Embedded Method) | 0.8903 | 0.4844 | 0.0624 | 0.1413 | Random Forest Regression with Pearson Correlation (Filter Method) | 0.8917 | 0.4798 | 0.062 | 0.1419 | Random Forest Regression with Constant Feature Elimination (Filter Method) | 0.8912 | 0.4864 | 0.0622 | 0.141 | Random Forest Regression with Quasi-Constant (Filter Method) | 0.8907 | 0.4903 | 0.0623 | 0.1405 | Random Forest Regression with Correlated Features (Filter Method) | 0.8912 | 0.4864 | 0.0622 | 0.141 | Random Forest Regression with Duplicate Features (Filter Method) | 0.8912 | 0.4864 | 0.0622 | 0.141 |
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