SVR vs. Ridge, LASSO, and ElasticNet: Performance Comparison

This analysis compares the performance of Support Vector Regression (SVR) to Ridge, LASSO, and ElasticNet regression models based on various metrics like RMSE, MAE, MeAE, and R-squared. The data shows slight variations in performance across different metrics.

| Model | RMSE | MAE | MeAE | R-squared | |---|---|---|---|---| | Ridge | 9.766 | 7.698 | 6.599 | 0.016 | | LASSO | 9.773 | 7.692 | 6.650 | 0.015 | | ElasticNet | 9.769 | 7.699 | 6.676 | 0.016 | | SVR | 9.945 | 7.507 | 6.602 | -0.020 |

Here's a breakdown of the differences:

1. RMSE: SVR's RMSE (9.945) is higher than Ridge (9.766), LASSO (9.773), and ElasticNet (9.769), indicating greater average error for SVR.

2. MAE and MeAE: SVR (7.507 and 6.602) has lower MAE and MeAE compared to Ridge (7.698 and 6.599), LASSO (7.692 and 6.650), and ElasticNet (7.699 and 6.676), suggesting lower prediction errors for SVR.

3. R-squared: While all models have close R-squared values, SVR's value (-0.020) is negative, unlike the near-zero values for the other three models. Negative R-squared signifies a poorer model fit.

In summary, SVR exhibits a slight advantage in terms of prediction error compared to the other three models, but it falls short in terms of fitting capability. It's crucial to note that these conclusions are based on the provided data and evaluation metrics. The relative performance of models can vary depending on the dataset and specific problem. Therefore, it's important to consider different metrics and specific context when choosing a model for a particular application.