Table 2.
Summary of the different methods compared in this work.
| Type | Rejection | ||
|---|---|---|---|
| Le Phare | Template-fitting | Weak | |
| CPz | Random forest classification + template-fitting | Weak | |
| Phosphoros | Template-fitting | No | |
| EAzY | Template-fitting | Strong | |
| METAPHOR | Machine-learning: neural network | Strong | |
| ANNz | Machine-learning: neural network | No | |
| GPz | Machine-learning: Gaussian processes | Weak | |
| GBRT | Machine-learning: boosted decision trees | Weak | |
| RF | Machine-learning: random forest | No | |
| Adaboost | Machine-learning: boosted decision trees | No | |
| DNF | Machine-learning: nearest neighbor | Strong | |
| frankenz | Machine-learning: nearest neighbor | Strong | |
| NNPZ | Machine-learning: nearest neighbor | No |
Notes. Columns are: the name of the code, the type of the approach (template-fitting or machine-learning) and whether a rejection of the results is applied or not. We qualify as strong a rejection of more than 15% of the full spectroscopic sample (more than 10 594 sources remaining; see Table B.1), otherwise it is considered to be a weak one.
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