| sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transfo
rmer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(missingindicator=s
klearn.impute.MissingIndicator,imputer=sklearn.preprocessing.imputation.Imp
uter,standardscaler=sklearn.preprocessing.data.StandardScaler),nominal=skle
arn.pipeline.Pipeline(simpleimputer=sklearn.impute.SimpleImputer,onehotenco
der=sklearn.preprocessing._encoders.OneHotEncoder)),adaboostclassifier=skle
arn.ensemble.weight_boosting.AdaBoostClassifier(base_estimator=sklearn.tree
.tree.DecisionTreeClassifier))(2) | Pipeline of transforms with a final estimator.
Sequentially apply a list of transforms and a final estimator.
Intermediate steps of the pipeline must be 'transforms', that is, they
must implement fit and transform methods.
The final estimator only needs to implement fit.
The transformers in the pipeline can be cached using ``memory`` argument.
The purpose of the pipeline is to assemble several steps that can be
cross-validated together while setting different parameters.
For this, it enables setting parameters of the various steps using their
names and the parameter name separated by a '__', as in the example below.
A step's estimator may be replaced entirely by setting the parameter
with its name to another estimator, or a transformer removed by setting
to None. |
| sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(missingindicator=sklearn.impute.MissingIndicator,imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=sklearn.preprocessing.data.StandardScaler),nominal=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)),adaboostclassifier=sklearn.ensemble.weight_boosting.AdaBoostClassifier(base_estimator=sklearn.tree.tree.DecisionTreeClassifier))(2)_memory | null |
| sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(missingindicator=sklearn.impute.MissingIndicator,imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=sklearn.preprocessing.data.StandardScaler),nominal=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)),adaboostclassifier=sklearn.ensemble.weight_boosting.AdaBoostClassifier(base_estimator=sklearn.tree.tree.DecisionTreeClassifier))(2)_steps | [{"oml-python:serialized_object": "component_reference", "value": {"key": "columntransformer", "step_name": "columntransformer"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "adaboostclassifier", "step_name": "adaboostclassifier"}}] |
| sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(missingindicator=sklearn.impute.MissingIndicator,imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=sklearn.preprocessing.data.StandardScaler),nominal=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder))(2)_n_jobs | null |
| sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(missingindicator=sklearn.impute.MissingIndicator,imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=sklearn.preprocessing.data.StandardScaler),nominal=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder))(2)_remainder | "passthrough" |
| sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(missingindicator=sklearn.impute.MissingIndicator,imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=sklearn.preprocessing.data.StandardScaler),nominal=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder))(2)_sparse_threshold | 0.3 |
| sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(missingindicator=sklearn.impute.MissingIndicator,imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=sklearn.preprocessing.data.StandardScaler),nominal=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder))(2)_transformer_weights | null |
| sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(missingindicator=sklearn.impute.MissingIndicator,imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=sklearn.preprocessing.data.StandardScaler),nominal=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder))(2)_transformers | [{"oml-python:serialized_object": "component_reference", "value": {"key": "numeric", "step_name": "numeric", "argument_1": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13]}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "nominal", "step_name": "nominal", "argument_1": []}}] |
| sklearn.pipeline.Pipeline(missingindicator=sklearn.impute.MissingIndicator,imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=sklearn.preprocessing.data.StandardScaler)(2)_memory | null |
| sklearn.pipeline.Pipeline(missingindicator=sklearn.impute.MissingIndicator,imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=sklearn.preprocessing.data.StandardScaler)(2)_steps | [{"oml-python:serialized_object": "component_reference", "value": {"key": "missingindicator", "step_name": "missingindicator"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "imputer", "step_name": "imputer"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "standardscaler", "step_name": "standardscaler"}}] |
| sklearn.impute.MissingIndicator(3)_error_on_new | false |
| sklearn.impute.MissingIndicator(3)_features | "missing-only" |
| sklearn.impute.MissingIndicator(3)_missing_values | NaN |
| sklearn.impute.MissingIndicator(3)_sparse | "auto" |
| sklearn.preprocessing.imputation.Imputer(51)_axis | 0 |
| sklearn.preprocessing.imputation.Imputer(51)_copy | true |
| sklearn.preprocessing.imputation.Imputer(51)_missing_values | "NaN" |
| sklearn.preprocessing.imputation.Imputer(51)_strategy | "median" |
| sklearn.preprocessing.imputation.Imputer(51)_verbose | 0 |
| sklearn.preprocessing.data.StandardScaler(38)_copy | true |
| sklearn.preprocessing.data.StandardScaler(38)_with_mean | true |
| sklearn.preprocessing.data.StandardScaler(38)_with_std | true |
| sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)(6)_memory | null |
| sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)(6)_steps | [{"oml-python:serialized_object": "component_reference", "value": {"key": "simpleimputer", "step_name": "simpleimputer"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "onehotencoder", "step_name": "onehotencoder"}}] |
| sklearn.impute.SimpleImputer(18)_copy | true |
| sklearn.impute.SimpleImputer(18)_fill_value | -1 |
| sklearn.impute.SimpleImputer(18)_missing_values | NaN |
| sklearn.impute.SimpleImputer(18)_strategy | "constant" |
| sklearn.impute.SimpleImputer(18)_verbose | 0 |
| sklearn.preprocessing._encoders.OneHotEncoder(20)_categorical_features | null |
| sklearn.preprocessing._encoders.OneHotEncoder(20)_categories | null |
| sklearn.preprocessing._encoders.OneHotEncoder(20)_dtype | {"oml-python:serialized_object": "type", "value": "np.float64"} |
| sklearn.preprocessing._encoders.OneHotEncoder(20)_handle_unknown | "ignore" |
| sklearn.preprocessing._encoders.OneHotEncoder(20)_n_values | null |
| sklearn.preprocessing._encoders.OneHotEncoder(20)_sparse | true |
| sklearn.ensemble.weight_boosting.AdaBoostClassifier(base_estimator=sklearn.tree.tree.DecisionTreeClassifier)(14)_algorithm | "SAMME" |
| sklearn.ensemble.weight_boosting.AdaBoostClassifier(base_estimator=sklearn.tree.tree.DecisionTreeClassifier)(14)_learning_rate | 0.8258296169139974 |
| sklearn.ensemble.weight_boosting.AdaBoostClassifier(base_estimator=sklearn.tree.tree.DecisionTreeClassifier)(14)_n_estimators | 429 |
| sklearn.ensemble.weight_boosting.AdaBoostClassifier(base_estimator=sklearn.tree.tree.DecisionTreeClassifier)(14)_random_state | 53944 |
| sklearn.tree.tree.DecisionTreeClassifier(63)_class_weight | null |
| sklearn.tree.tree.DecisionTreeClassifier(63)_criterion | "gini" |
| sklearn.tree.tree.DecisionTreeClassifier(63)_max_depth | 3 |
| sklearn.tree.tree.DecisionTreeClassifier(63)_max_features | null |
| sklearn.tree.tree.DecisionTreeClassifier(63)_max_leaf_nodes | null |
| sklearn.tree.tree.DecisionTreeClassifier(63)_min_impurity_decrease | 0.0 |
| sklearn.tree.tree.DecisionTreeClassifier(63)_min_impurity_split | null |
| sklearn.tree.tree.DecisionTreeClassifier(63)_min_samples_leaf | 1 |
| sklearn.tree.tree.DecisionTreeClassifier(63)_min_samples_split | 2 |
| sklearn.tree.tree.DecisionTreeClassifier(63)_min_weight_fraction_leaf | 0.0 |
| sklearn.tree.tree.DecisionTreeClassifier(63)_presort | false |
| sklearn.tree.tree.DecisionTreeClassifier(63)_random_state | 16546 |
| sklearn.tree.tree.DecisionTreeClassifier(63)_splitter | "best" |