Model Fitness Evaluation: RMSE, MAE, MeAE, R-squared for Ridge, LASSO, and ElasticNet

To evaluate the fitness of the models, let's interpret the values of the measures calculated for the Ridge, LASSO, and ElasticNet models:

1. RMSE (Root Mean Squared Error): The RMSE values for the Ridge, LASSO, and ElasticNet models are 9.7655, 9.7731, and 9.7689, respectively. RMSE measures the average difference between the predicted values and the actual values. A lower RMSE indicates better model performance. In this case, the RMSE values suggest that, on average, the predictions made by the models are approximately 9.7655, 9.7731, and 9.7689 units away from the actual values, respectively.

2. MAE (Mean Absolute Error): The MAE values for the Ridge, LASSO, and ElasticNet models are 7.6979, 7.6920, and 7.6990, respectively. MAE measures the average absolute difference between the predicted values and the actual values. Similar to RMSE, a lower MAE indicates better model performance. The MAE values indicate that, on average, the predictions made by the models are approximately 7.6979, 7.6920, and 7.6990 units away from the actual values, respectively.

3. MeAE (Median Absolute Error): The MeAE values for the Ridge, LASSO, and ElasticNet models are 6.5989, 6.6496, and 6.6757, respectively. MeAE measures the median absolute difference between the predicted values and the actual values. It is less sensitive to outliers compared to MAE. Similar to RMSE and MAE, a lower MeAE indicates better model performance. The MeAE values indicate that, on average, the predictions made by the models are approximately 6.5989, 6.6496, and 6.6757 units away from the actual values, respectively.

4. R-squared: The R-squared values for the Ridge, LASSO, and ElasticNet models are 0.0164, 0.0149, and 0.0157, respectively. R-squared measures the proportion of the variance in the dependent variable that is predictable from the independent variables. It ranges from 0 to 1, where 1 indicates a perfect fit. A higher R-squared value indicates a better fit. In this case, the R-squared values are relatively low, indicating that the models explain only a small portion of the variance in the dependent variable.

In summary, the values of these measures indicate that the Ridge, LASSO, and ElasticNet models have similar performance in terms of accuracy and fit. The RMSE, MAE, and MeAE values are relatively high, indicating a noticeable difference between the predicted values and the actual values. Additionally, the low R-squared values suggest that the models do not explain much of the variance in the dependent variable. This indicates that these models may not be the best fit for the data in this case.