Regression Performance
Outputs Overview
| Outputs | ||||
|---|---|---|---|---|
| Reference | impervious | vegetation | soil | water |
| Prediction | impervious | vegetation | soil | water |
Metrics
Number of samples: 1549
| Outputs | ||||
|---|---|---|---|---|
| impervious | vegetation | soil | water | |
| Mean absolute error (MAE) | 0.038 | 0.0384 | 0.0066 | 0.0026 |
| Root MSE (RMSE) | 0.0564 | 0.0558 | 0.0261 | 0.019 |
| Mean error (ME) | -0.0017 | 0.0005 | 0.0009 | 0.0003 |
| Mean squared error (MSE) | 0.0032 | 0.0031 | 0.0007 | 0.0004 |
| Median absolute error (MedAE) | 0.028 | 0.028 | 0.0 | 0.0 |
| Squared pearson correlation (r^2) | 0.9705 | 0.9682 | 0.9644 | 0.9811 |
| Explained variance score | 0.97 | 0.9675 | 0.9624 | 0.9809 |
| Coefficient of determination (R^2) | 0.97 | 0.9674 | 0.9624 | 0.9809 |
See Scikit-Learn documentation for details.
See Scipy documentation for details on pearson correlation.
Scatter and Residuals Plots
