ML algorithms | Hyper parameters | Importance |
---|---|---|
XGB | ‘min_chid_weigh’ = 4’max_depht’ = 12,’learning_rate’ = 0.4, ‘gamma’ = 0.6, ‘colsample_bytree’ = 0.9 | 0.88 |
K-NN | (leaf_size = list(range(1,20)), n_neighbors = list(range(1,9)), p = [1, 2]) | 0.73 |
AdaBoost | (“random_state”: 924, “n_estimators”: 92, “learning rate”: 0.4, “algorithm”: “samme.R”) | 0.89 |
HGB | (‘verbose’ = 4, ‘random_state’ = 84, ‘max_leaf_nodes’ = 78, ‘max_iter’ = 180, ‘max_depht’ = 11, ‘learning rate’ = 0.8) | 0.94 |