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Table 1.
Applied regression models with RMSEtest errors using all the attributes of galaxies and without the VLG value.
| Model | All | Without | SGB | VLG | log d25 | bri25 | U | B | I | K | U–I | B–K |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| attributes | VLG | SGL | ||||||||||
| Linear | 0.38 | 0.52 | 0.48 | 37 | 0.22 | 0.64 | 0.01 | 0 | 0 | 0.01 | 0.14 | 0.02 |
| Polynomial | 0.37 | 0.49 | 0.87 | 32 | 0.27 | 1.16 | 0.78 | 0.06 | 0 | 0.05 | 1.67 | 0.05 |
| k-NN | 0.37 | 0.50 | 0 | 35 | 0.01 | 0 | 0 | 0.01 | 0.02 | 0 | 0 | 0 |
| Gradient boosting | 0.36 | 0.44 | 1.51 | 22 | 0.18 | 0 | 0.04 | 1.02 | 0.95 | 0 | 0 | 0 |
| ANN | 0.35 | 0.44 | 1.09 | 26 | 0.33 | 0 | 0.16 | 0.01 | 0.47 | 0.11 | 0.71 | 0.22 |
Notes.
The last ten columns show the importance of each attribute for a given regression model, which is given as an increase in error as a percentage when leaving out a given attribute.
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