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.
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