Table 2.
Classifying GCs from the Virgo and Fornax tabular data sets.
| Method | TPR | FPR | FDR | AUC ROC |
|---|---|---|---|---|
| Logistic Regression | 0.761±0.007 | 0.054±0.002 | 0.204±0.005 | 0.951±0.001 |
| Support Vector Machine (linear) | 0.778±0.007 | 0.053±0.002 | 0.196±0.005 | – |
| Support Vector Machine (radial) | 0.858±0.005 | 0.042±0.002 | 0.151±0.006 | – |
| Nearest Neighbour | 0.897±0.005 | 0.040±0.002 | 0.139±0.007 | – |
| 12 Nearest Neighbours | 0.914±0.005 | 0.030±0.001 | 0.100±0.004 | 0.987±0.001 |
| Decision Tree | 0.892±0.009 | 0.036±0.003 | 0.127±0.010 | 0.983±0.001 |
| Random Forest | 0.909±0.005 | 0.018±0.001 | 0.066±0.005 | 0.992±0.001 |
| AdaBoost | 0.910±0.005 | 0.018±0.001 | 0.065±0.005 | 0.992±0.001 |
| CatBoost | 0.920±0.006 | 0.025±0.002 | 0.089±0.007 | 0.990±0.001 |
| Neural Network (29-1) | 0.756±0.007 | 0.056±0.002 | 0.210±0.005 | 0.948±0.001 |
| Neural Network (29-100-100-1) | 0.932±0.009 | 0.028±0.003 | 0.097±0.010 | 0.992±0.001 |
Notes. The reported results are averages over ten random splits (train, validation and test). Uncertainties are given as standard deviations and the used decision threshold is 0.5. Support vector machines and nearest neighbour only provide a class label and hence, no AUC ROC is reported. For neural networks, the number of neurons per layer is given in brackets.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.