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Table E.1.
Training machine learning methods on tabular data of Fornax and evaluating it on Virgo data.
| averaged per galaxy | average over all sources | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| Method | TPR | FPR | FDR | AUC ROC | TPR | FPR | FDR | AUC ROC | # TPs | # FPs |
| Logistic Regression | 0.73 | 0.15 | 0.58 | 0.88 | 0.83 | 0.16 | 0.44 | 0.91 | 10241 | 8079 |
| Support Vector Machine (linear) | 0.76 | 0.14 | 0.56 | – | 0.84 | 0.14 | 0.41 | – | 10405 | 7293 |
| Support Vector Machine (radial) | 0.83 | 0.05 | 0.33 | – | 0.90 | 0.04 | 0.16 | – | 11213 | 2165 |
| Nearest Neighbour | 0.85 | 0.04 | 0.28 | − | 0.89 | 0.04 | 0.15 | − | 11031 | 1882 |
| 12 Nearest Neighbours | 0.87 | 0.03 | 0.22 | 0.98 | 0.91 | 0.03 | 0.10 | 0.99 | 11338 | 1300 |
| Decision Tree | 0.84 | 0.03 | 0.26 | 0.95 | 0.88 | 0.03 | 0.12 | 0.97 | 10889 | 1520 |
| Random Forest | 0.84 | 0.02 | 0.14 | 0.98 | 0.89 | 0.01 | 0.06 | 0.99 | 11086 | 698 |
| AdaBoost | 0.84 | 0.02 | 0.14 | 0.98 | 0.89 | 0.01 | 0.06 | 0.99 | 11082 | 700 |
| CatBoost | 0.85 | 0.02 | 0.17 | 0.98 | 0.90 | 0.02 | 0.08 | 0.99 | 11169 | 908 |
| Neural Network (29-1) | 0.73 | 0.17 | 0.60 | 0.87 | 0.82 | 0.19 | 0.49 | 0.89 | 10193 | 9746 |
| Neural Network (29-100-100-1) | 0.88 | 0.02 | 0.17 | 0.99 | 0.92 | 0.02 | 0.08 | 0.99 | 11443 | 937 |
Notes. The Virgo data contains in total 63162 sources with 12395 catalogued GCs. Results are given for a decision threshold of 0.5. Similar to Table 3.
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