| sklearn.pipeline.Pipeline(imputation=openmlstudy14.preprocessing.Conditiona
lImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,variencethres
hold=sklearn.feature_selection.variance_threshold.VarianceThreshold,classif
ier=sklearn.ensemble.forest.RandomForestClassifier)(1) | Automatically created scikit-learn flow. |
| sklearn.ensemble.forest.RandomForestClassifier(21)_bootstrap | false |
| sklearn.ensemble.forest.RandomForestClassifier(21)_class_weight | null |
| sklearn.ensemble.forest.RandomForestClassifier(21)_criterion | "entropy" |
| sklearn.ensemble.forest.RandomForestClassifier(21)_max_depth | null |
| sklearn.ensemble.forest.RandomForestClassifier(21)_max_features | 0.5705511374753641 |
| sklearn.ensemble.forest.RandomForestClassifier(21)_max_leaf_nodes | null |
| sklearn.ensemble.forest.RandomForestClassifier(21)_min_impurity_split | 1e-07 |
| sklearn.ensemble.forest.RandomForestClassifier(21)_min_samples_leaf | 1 |
| sklearn.ensemble.forest.RandomForestClassifier(21)_min_samples_split | 12 |
| sklearn.ensemble.forest.RandomForestClassifier(21)_min_weight_fraction_leaf | 0.0 |
| sklearn.ensemble.forest.RandomForestClassifier(21)_n_estimators | 100 |
| sklearn.ensemble.forest.RandomForestClassifier(21)_n_jobs | 1 |
| sklearn.ensemble.forest.RandomForestClassifier(21)_oob_score | false |
| sklearn.ensemble.forest.RandomForestClassifier(21)_random_state | 22404 |
| sklearn.ensemble.forest.RandomForestClassifier(21)_verbose | 0 |
| sklearn.ensemble.forest.RandomForestClassifier(21)_warm_start | false |
| openmlstudy14.preprocessing.ConditionalImputer(2)_axis | 0 |
| openmlstudy14.preprocessing.ConditionalImputer(2)_categorical_features | [4, 6, 9, 11, 12, 13, 14, 15] |
| openmlstudy14.preprocessing.ConditionalImputer(2)_copy | true |
| openmlstudy14.preprocessing.ConditionalImputer(2)_fill_empty | 0 |
| openmlstudy14.preprocessing.ConditionalImputer(2)_missing_values | "NaN" |
| openmlstudy14.preprocessing.ConditionalImputer(2)_strategy | "mean" |
| openmlstudy14.preprocessing.ConditionalImputer(2)_strategy_nominal | "most_frequent" |
| openmlstudy14.preprocessing.ConditionalImputer(2)_verbose | 0 |
| sklearn.preprocessing.data.OneHotEncoder(7)_categorical_features | [4, 6, 9, 11, 12, 13, 14, 15] |
| sklearn.preprocessing.data.OneHotEncoder(7)_dtype | {"oml-python:serialized_object": "type", "value": "np.float64"} |
| sklearn.preprocessing.data.OneHotEncoder(7)_handle_unknown | "ignore" |
| sklearn.preprocessing.data.OneHotEncoder(7)_n_values | "auto" |
| sklearn.preprocessing.data.OneHotEncoder(7)_sparse | true |
| sklearn.feature_selection.variance_threshold.VarianceThreshold(4)_threshold | 0.0 |