R-squared (0.9564): This indicates a very high proportion of variance in the dependent variable (total jobs) is predictable from the independent variables in the model.
Adjusted R-squared (0.9213): This is a modified version of R-squared adjusted for the number of predictors in the model, still indicating a good fit.
MAE (Mean Absolute Error): The average absolute error of the predictions is 3889 jobs.
MSE (Mean Squared Error): The average squared difference between the estimated values and the actual value is 2,229,292.5, a measure that gives higher weight to larger errors.
RMSE (Root Mean Squared Error): The square root of MSE, which is 4708 jobs, gives an idea of the magnitude of the errors in the same units as the dependent variable (total jobs).
R-squared (0.9564): This value is very high, suggesting the model explains a large proportion of the variance in the validation dataset.
Adjusted R-squared (0.9213): This is also high, indicating that the number of predictors in the model is appropriate for data and the model fits the validation data well.
MAE (Mean Absolute Error) (3888.99): On average, the model’s predictions are off by approximately 3889 jobs from the actual values.
MSE (Mean Squared Error) (2,229,292.5): This is relatively high, influenced by the squared nature of the metric which gives more weight to larger errors.
RMSE (Root Mean Squared Error) (4708.31): This is the square root of the MSE and provides an error term in the same units as the predicted variable (total jobs). This value suggests that typical predictions are within approximately 4708 jobs of the actual values.