| sklearn.pipeline.Pipeline(imputation=hyperimp.utils.preprocessing.Condition
alImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,variencethre
shold=sklearn.feature_selection.variance_threshold.VarianceThreshold,clf=sk
learn.ensemble.forest.RandomForestClassifier)(1) | Automatically created scikit-learn flow. |
| sklearn.preprocessing.data.OneHotEncoder(17)_categorical_features | [0, 3, 4, 5, 7, 8, 10, 11] |
| 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.ensemble.forest.RandomForestClassifier(32)_bootstrap | true |
| sklearn.ensemble.forest.RandomForestClassifier(32)_class_weight | null |
| sklearn.ensemble.forest.RandomForestClassifier(32)_criterion | "gini" |
| sklearn.ensemble.forest.RandomForestClassifier(32)_max_depth | null |
| sklearn.ensemble.forest.RandomForestClassifier(32)_max_features | 0.6336924569927115 |
| sklearn.ensemble.forest.RandomForestClassifier(32)_max_leaf_nodes | null |
| sklearn.ensemble.forest.RandomForestClassifier(32)_min_impurity_decrease | 0.0 |
| sklearn.ensemble.forest.RandomForestClassifier(32)_min_impurity_split | null |
| sklearn.ensemble.forest.RandomForestClassifier(32)_min_samples_leaf | 10 |
| sklearn.ensemble.forest.RandomForestClassifier(32)_min_samples_split | 14 |
| sklearn.ensemble.forest.RandomForestClassifier(32)_min_weight_fraction_leaf | 0.0 |
| sklearn.ensemble.forest.RandomForestClassifier(32)_n_estimators | 500 |
| sklearn.ensemble.forest.RandomForestClassifier(32)_n_jobs | 1 |
| sklearn.ensemble.forest.RandomForestClassifier(32)_oob_score | false |
| sklearn.ensemble.forest.RandomForestClassifier(32)_random_state | 1 |
| sklearn.ensemble.forest.RandomForestClassifier(32)_verbose | 0 |
| sklearn.ensemble.forest.RandomForestClassifier(32)_warm_start | false |
| sklearn.pipeline.Pipeline(imputation=hyperimp.utils.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,variencethreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold,clf=sklearn.ensemble.forest.RandomForestClassifier)(1)_memory | null |
| hyperimp.utils.preprocessing.ConditionalImputer(1)_axis | 0 |
| hyperimp.utils.preprocessing.ConditionalImputer(1)_categorical_features | [0, 3, 4, 5, 7, 8, 10, 11] |
| 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 |