StackGenVis: Alignment of Data, Algorithms, and Models for Stacking Ensemble Learning Using Performance Metrics
https://doi.org/10.1109/TVCG.2020.3030352
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8.4 KiB
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init=None,\n learning_rate=0.12, loss='deviance', max_depth=3,\n max_features=None, max_leaf_nodes=None,\n min_impurity_decrease=0.0, min_impurity_split=None,\n min_samples_leaf=1, min_samples_split=2,\n min_weight_fraction_leaf=0.0, n_estimators=114,\n n_iter_no_change=None, presort='deprecated',\n random_state=42, subsample=1.0, tol=0.0001,\n validation_fraction=0.1, verbose=0,\n warm_start=False)", "params": "{'n_estimators': [85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114], 'learning_rate': [0.01, 0.12], 'criterion': ['friedman_mse', 'mse', 'mae']}", "eachAlgor": "'GradB'", "AlgorithmsIDsEnd": "2926", "toggle": "0"}} |