| sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transfo
rmer.ColumnTransformer(cat=sklearn.pipeline.Pipeline(anothersimpleimputer=_
_main__.AnotherSimpleImputer,onehotencoder=sklearn.preprocessing._encoders.
OneHotEncoder),cont=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute.
_base.SimpleImputer,standardscaler=sklearn.preprocessing.data.StandardScale
r)),decisiontreeclassifier=sklearn.tree.tree.DecisionTreeClassifier)(1) | 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
it to 'passthrough' or ``None``. |
| sklearn.preprocessing.data.StandardScaler(31)_copy | true |
| sklearn.preprocessing.data.StandardScaler(31)_with_mean | true |
| sklearn.preprocessing.data.StandardScaler(31)_with_std | true |
| sklearn.preprocessing._encoders.OneHotEncoder(13)_categories | "auto" |
| sklearn.preprocessing._encoders.OneHotEncoder(13)_drop | null |
| sklearn.preprocessing._encoders.OneHotEncoder(13)_dtype | {"oml-python:serialized_object": "type", "value": "np.float64"} |
| sklearn.preprocessing._encoders.OneHotEncoder(13)_handle_unknown | "ignore" |
| sklearn.preprocessing._encoders.OneHotEncoder(13)_sparse | true |
| sklearn.tree.tree.DecisionTreeClassifier(51)_ccp_alpha | 0.0 |
| sklearn.tree.tree.DecisionTreeClassifier(51)_class_weight | null |
| sklearn.tree.tree.DecisionTreeClassifier(51)_criterion | "gini" |
| sklearn.tree.tree.DecisionTreeClassifier(51)_max_depth | 1 |
| sklearn.tree.tree.DecisionTreeClassifier(51)_max_features | null |
| sklearn.tree.tree.DecisionTreeClassifier(51)_max_leaf_nodes | null |
| sklearn.tree.tree.DecisionTreeClassifier(51)_min_impurity_decrease | 0.0 |
| sklearn.tree.tree.DecisionTreeClassifier(51)_min_impurity_split | null |
| sklearn.tree.tree.DecisionTreeClassifier(51)_min_samples_leaf | 1 |
| sklearn.tree.tree.DecisionTreeClassifier(51)_min_samples_split | 2 |
| sklearn.tree.tree.DecisionTreeClassifier(51)_min_weight_fraction_leaf | 0.0 |
| sklearn.tree.tree.DecisionTreeClassifier(51)_presort | "deprecated" |
| sklearn.tree.tree.DecisionTreeClassifier(51)_random_state | 38157 |
| sklearn.tree.tree.DecisionTreeClassifier(51)_splitter | "best" |
| sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing.data.StandardScaler)(2)_memory | null |
| sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing.data.StandardScaler)(2)_steps | [{"oml-python:serialized_object": "component_reference", "value": {"key": "simpleimputer", "step_name": "simpleimputer"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "standardscaler", "step_name": "standardscaler"}}] |
| sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing.data.StandardScaler)(2)_verbose | false |
| sklearn.impute._base.SimpleImputer(5)_add_indicator | false |
| sklearn.impute._base.SimpleImputer(5)_copy | true |
| sklearn.impute._base.SimpleImputer(5)_fill_value | null |
| sklearn.impute._base.SimpleImputer(5)_missing_values | NaN |
| sklearn.impute._base.SimpleImputer(5)_strategy | "median" |
| sklearn.impute._base.SimpleImputer(5)_verbose | 0 |
| sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(cat=sklearn.pipeline.Pipeline(anothersimpleimputer=__main__.AnotherSimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder),cont=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing.data.StandardScaler)),decisiontreeclassifier=sklearn.tree.tree.DecisionTreeClassifier)(1)_memory | null |
| sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(cat=sklearn.pipeline.Pipeline(anothersimpleimputer=__main__.AnotherSimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder),cont=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing.data.StandardScaler)),decisiontreeclassifier=sklearn.tree.tree.DecisionTreeClassifier)(1)_steps | [{"oml-python:serialized_object": "component_reference", "value": {"key": "columntransformer", "step_name": "columntransformer"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "decisiontreeclassifier", "step_name": "decisiontreeclassifier"}}] |
| sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(cat=sklearn.pipeline.Pipeline(anothersimpleimputer=__main__.AnotherSimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder),cont=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing.data.StandardScaler)),decisiontreeclassifier=sklearn.tree.tree.DecisionTreeClassifier)(1)_verbose | false |
| sklearn.compose._column_transformer.ColumnTransformer(cat=sklearn.pipeline.Pipeline(anothersimpleimputer=__main__.AnotherSimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder),cont=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing.data.StandardScaler))(1)_n_jobs | null |
| sklearn.compose._column_transformer.ColumnTransformer(cat=sklearn.pipeline.Pipeline(anothersimpleimputer=__main__.AnotherSimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder),cont=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing.data.StandardScaler))(1)_remainder | "drop" |
| sklearn.compose._column_transformer.ColumnTransformer(cat=sklearn.pipeline.Pipeline(anothersimpleimputer=__main__.AnotherSimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder),cont=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing.data.StandardScaler))(1)_sparse_threshold | 0.3 |
| sklearn.compose._column_transformer.ColumnTransformer(cat=sklearn.pipeline.Pipeline(anothersimpleimputer=__main__.AnotherSimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder),cont=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing.data.StandardScaler))(1)_transformer_weights | null |
| sklearn.compose._column_transformer.ColumnTransformer(cat=sklearn.pipeline.Pipeline(anothersimpleimputer=__main__.AnotherSimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder),cont=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing.data.StandardScaler))(1)_transformers | [{"oml-python:serialized_object": "component_reference", "value": {"key": "cat", "step_name": "cat", "argument_1": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 20, 22, 24, 26, 28]}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "cont", "step_name": "cont", "argument_1": [0, 17, 19, 21, 23, 25, 27]}}] |
| sklearn.compose._column_transformer.ColumnTransformer(cat=sklearn.pipeline.Pipeline(anothersimpleimputer=__main__.AnotherSimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder),cont=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing.data.StandardScaler))(1)_verbose | false |
| sklearn.pipeline.Pipeline(anothersimpleimputer=__main__.AnotherSimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)(1)_memory | null |
| sklearn.pipeline.Pipeline(anothersimpleimputer=__main__.AnotherSimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)(1)_steps | [{"oml-python:serialized_object": "component_reference", "value": {"key": "anothersimpleimputer", "step_name": "anothersimpleimputer"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "onehotencoder", "step_name": "onehotencoder"}}] |
| sklearn.pipeline.Pipeline(anothersimpleimputer=__main__.AnotherSimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)(1)_verbose | false |
| __main__.AnotherSimpleImputer(1)_add_indicator | false |
| __main__.AnotherSimpleImputer(1)_copy | true |
| __main__.AnotherSimpleImputer(1)_fill_value | null |
| __main__.AnotherSimpleImputer(1)_missing_values | NaN |
| __main__.AnotherSimpleImputer(1)_strategy | "most_frequent" |
| __main__.AnotherSimpleImputer(1)_verbose | 0 |