Table 6. Performance of machine learning models in OVCAR-4 MCTS classifications with 2-ME inhibitor treatment. DT, decision tree. GB, gradient boosting. kNN, k nearest neighbor. LG, logistics. NB, naïve bayes. SVM, support vector machine. AH, agglomerative hierarchical. BC, birch. GM, Gaussian mixture. KM, k means. MBK, mini batch k-means. ST, spectral.
| Supervised | Unsupervised | |||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
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| Models | DT | GB | kNN | LG | NB | SVM | AH | BC | GM | KM | MBK | ST |
| Cross_Statistical | ||||||||||||
|
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| Accuracy | 0.77 | 0.91 | 0.83 | 0.97 | 0.63 | 0.99 | 0.56 | 0.56 | 0.58 | 0.62 | 0.70 | 0.31 |
| Precision | 0.80 | 0.92 | 0.86 | 0.97 | 0.66 | 0.99 | / | / | / | / | / | / |
| Recall | 0.77 | 0.91 | 0.83 | 0.97 | 0.63 | 0.99 | / | / | / | / | / | / |
| F1-score | 0.77 | 0.91 | 0.84 | 0.97 | 0.61 | 0.99 | / | / | / | / | / | / |
| Silhouette | / | / | / | / | / | / | 0.11 | 0.11 | 0.15 | 0.15 | 0.14 | 0.22 |
| Homogeneity | / | / | / | / | / | / | 0.37 | 0.37 | 0.37 | 0.42 | 0.55 | 0.06 |
| Completeness | / | / | / | / | / | / | 0.51 | 0.51 | 0.42 | 0.45 | 0.56 | 0.33 |
| V_meaure | / | / | / | / | / | / | 0.42 | 0.42 | 0.39 | 0.44 | 0.55 | 0.11 |
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| Cross_Screening | ||||||||||||
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| Accuracy | 0.86 | 0.94 | 0.86 | 0.80 | 0.70 | 0.85 | 0.63 | 0.63 | 0.58 | 0.58 | 0.60 | 0.63 |
| Precision | 0.87 | 0.94 | 0.88 | 0.81 | 0.73 | 0.86 | / | / | / | / | / | / |
| Recall | 0.86 | 0.94 | 0.86 | 0.80 | 0.70 | 0.85 | / | / | / | / | / | / |
| F1-score | 0.86 | 0.94 | 0.86 | 0.80 | 0.70 | 0.85 | / | / | / | / | / | / |
| Silhouette | / | / | / | / | / | / | 0.26 | 0.26 | 0.26 | 0.26 | 0.26 | 0.26 |
| Homogeneity | / | / | / | / | / | / | 0.55 | 0.55 | 0.51 | 0.45 | 0.48 | 0.51 |
| Completeness | / | / | / | / | / | / | 0.65 | 0.65 | 0.70 | 0.50 | 0.54 | 0.62 |
| V_meaure | / | / | / | / | / | / | 0.60 | 0.60 | 0.59 | 0.47 | 0.51 | 0.56 |
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| Composite_Hyperparameter | ||||||||||||
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| Accuracy | 0.73 | 0.91 | 0.86 | 0.80 | 0.68 | 0.80 | 0.65 | 0.65 | 0.61 | 0.58 | 0.59 | 0.64 |
| Precision | 0.74 | 0.92 | 0.87 | 0.81 | 0.70 | 0.81 | / | / | / | / | / | / |
| Recall | 0.73 | 0.91 | 0.86 | 0.80 | 0.68 | 0.80 | / | / | / | / | / | / |
| F1-score | 0.73 | 0.91 | 0.86 | 0.79 | 0.68 | 0.79 | / | / | / | / | / | / |
| Silhouette | / | / | / | / | / | / | 0.29 | 0.29 | 0.32 | 0.32 | .031 | 0.32 |
| Homogeneity | / | / | / | / | / | / | 0.50 | 0.50 | 0.50 | .046 | 0.46 | 0.52 |
| Completeness | / | / | / | / | / | / | 0.54 | 0.54 | 0.55 | 0.50 | 0.50 | 0.61 |
| V_meaure | / | / | / | / | / | / | 0.52 | 0.52 | 0.52 | 0.48 | 0.48 | 0.56 |