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Machine learning algorithms and hyper-parameters.
| Algorithm | Hyper-parameter | Values |
|---|---|---|
| KNN | n_neighbors | 1, 3, 5, 7, 9 |
| Linear SVC | C | 0.1, 1, 10 |
| SVC (RBF kernel) | C gamma |
0.1, 1, 10 0.01, 0.1, 1, 10 |
| Gaussian Naive Bayes | * no hyper-parameter | |
| Bernoulli Naive Bayes | * no hyper-parameter | |
| Logistic Regression | C penalty |
0.1, 1, 10 l1, l2 |
| Random Forest | max_depth min_sample_split |
3, 5, 7 3, 5 |
| XGBoost | max_depth min_child_weight gamma |
3, 5, 7 1, 3, 5 0.01, 0.1, 1, 10 |
