| sklearn.pipeline.Pipeline(imputation=hyperimp.utils.preprocessing.Condition
alImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,scaling=skle
arn.preprocessing.data.StandardScaler,variencethreshold=sklearn.feature_sel
ection.variance_threshold.VarianceThreshold,clf=sklearn.svm.classes.SVC)(1) | Automatically created scikit-learn flow. |
| sklearn.preprocessing.data.OneHotEncoder(17)_categorical_features | [0, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 15, 16, 17, 20, 34, 36] |
| sklearn.preprocessing.data.OneHotEncoder(17)_dtype | {"oml-python:serialized_object": "type", "value": "np.float64"} |
| sklearn.preprocessing.data.OneHotEncoder(17)_handle_unknown | "ignore" |
| sklearn.preprocessing.data.OneHotEncoder(17)_n_values | "auto" |
| sklearn.preprocessing.data.OneHotEncoder(17)_sparse | true |
| sklearn.feature_selection.variance_threshold.VarianceThreshold(11)_threshold | 0.0 |
| sklearn.preprocessing.data.StandardScaler(5)_copy | true |
| sklearn.preprocessing.data.StandardScaler(5)_with_mean | false |
| sklearn.preprocessing.data.StandardScaler(5)_with_std | true |
| sklearn.svm.classes.SVC(16)_C | 90.17252593695522 |
| sklearn.svm.classes.SVC(16)_cache_size | 200 |
| sklearn.svm.classes.SVC(16)_class_weight | null |
| sklearn.svm.classes.SVC(16)_coef0 | 0.5747308015114087 |
| sklearn.svm.classes.SVC(16)_decision_function_shape | "ovr" |
| sklearn.svm.classes.SVC(16)_degree | 3 |
| sklearn.svm.classes.SVC(16)_gamma | 0.03969646711781232 |
| sklearn.svm.classes.SVC(16)_kernel | "rbf" |
| sklearn.svm.classes.SVC(16)_max_iter | -1 |
| sklearn.svm.classes.SVC(16)_probability | false |
| sklearn.svm.classes.SVC(16)_random_state | 1 |
| sklearn.svm.classes.SVC(16)_shrinking | false |
| sklearn.svm.classes.SVC(16)_tol | 0.0019903213700606195 |
| sklearn.svm.classes.SVC(16)_verbose | false |
| hyperimp.utils.preprocessing.ConditionalImputer(1)_axis | 0 |
| hyperimp.utils.preprocessing.ConditionalImputer(1)_categorical_features | [0, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 15, 16, 17, 20, 34, 36] |
| hyperimp.utils.preprocessing.ConditionalImputer(1)_copy | true |
| hyperimp.utils.preprocessing.ConditionalImputer(1)_fill_empty | 0 |
| hyperimp.utils.preprocessing.ConditionalImputer(1)_missing_values | "NaN" |
| hyperimp.utils.preprocessing.ConditionalImputer(1)_strategy | "mean" |
| hyperimp.utils.preprocessing.ConditionalImputer(1)_strategy_nominal | "most_frequent" |
| hyperimp.utils.preprocessing.ConditionalImputer(1)_verbose | 0 |
| sklearn.pipeline.Pipeline(imputation=hyperimp.utils.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,scaling=sklearn.preprocessing.data.StandardScaler,variencethreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold,clf=sklearn.svm.classes.SVC)(1)_memory | null |