Classification models | Hyper-parameter search spaces |
---|---|
Neural network (NN) | hidden_layer_sizes = {100, 200, 300}, learning_rate = 0.001 |
Support vector machines (SVM) | C = {0.01, 0.1, 1, 5, 10, 100}, kernel = {‘linear’, ‘rbf’}, gamma = {0.01, 0.1, 1, 10}, class_weight = {None, ‘balanced’} |
k-nearest neighbor (kNN) | n_neighbors = {1,3,5,7,9}, weights = {‘uniform’, ‘distance’} |
Decision tree (DT) | max_depth = {5, 6, 7, 8, 9, 10, 15, 20}, class_weight = {None, ‘balanced’} |
Random forest (RF) | n_estimators = {20, 50, 100, 200}, class_weight = {None, ‘balanced’, ‘balanced_subsample’} |
Gradient boosting (GB) | n_estimators = {20, 50, 100, 200} |
Logistic regression (LR) | C = {0.01, 0.1, 1, 5, 10, 100}, penalty = {‘l1’, ‘l2’}, class_weight = {None, ‘balanced’} |