| sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transfo
rmer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(imputer=sklearn.pr
eprocessing.imputation.Imputer,standardscaler=sklearn.preprocessing.data.St
andardScaler),nominal=sklearn.pipeline.Pipeline(simpleimputer=sklearn.imput
e.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder
)),variancethreshold=sklearn.feature_selection.variance_threshold.VarianceT
hreshold,svc=sklearn.svm.classes.SVC)(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.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=sklearn.preprocessing.data.StandardScaler),nominal=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder))(6)_n_jobs | null |
| sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=sklearn.preprocessing.data.StandardScaler),nominal=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder))(6)_remainder | "passthrough" |
| sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=sklearn.preprocessing.data.StandardScaler),nominal=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder))(6)_sparse_threshold | 0.3 |
| sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=sklearn.preprocessing.data.StandardScaler),nominal=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder))(6)_transformer_weights | null |
| sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=sklearn.preprocessing.data.StandardScaler),nominal=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder))(6)_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, 14, 15]}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "nominal", "step_name": "nominal", "argument_1": []}}] |
| sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=sklearn.preprocessing.data.StandardScaler)(9)_memory | null |
| sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=sklearn.preprocessing.data.StandardScaler)(9)_steps | [{"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.preprocessing.imputation.Imputer(56)_axis | 0 |
| sklearn.preprocessing.imputation.Imputer(56)_copy | true |
| sklearn.preprocessing.imputation.Imputer(56)_missing_values | "NaN" |
| sklearn.preprocessing.imputation.Imputer(56)_strategy | "mean" |
| sklearn.preprocessing.imputation.Imputer(56)_verbose | 0 |
| sklearn.preprocessing.data.StandardScaler(44)_copy | true |
| sklearn.preprocessing.data.StandardScaler(44)_with_mean | true |
| sklearn.preprocessing.data.StandardScaler(44)_with_std | true |
| sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)(7)_memory | null |
| sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)(7)_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(19)_copy | true |
| sklearn.impute.SimpleImputer(19)_fill_value | -1 |
| sklearn.impute.SimpleImputer(19)_missing_values | NaN |
| sklearn.impute.SimpleImputer(19)_strategy | "constant" |
| sklearn.impute.SimpleImputer(19)_verbose | 0 |
| sklearn.preprocessing._encoders.OneHotEncoder(28)_categorical_features | null |
| sklearn.preprocessing._encoders.OneHotEncoder(28)_categories | null |
| sklearn.preprocessing._encoders.OneHotEncoder(28)_dtype | {"oml-python:serialized_object": "type", "value": "np.float64"} |
| sklearn.preprocessing._encoders.OneHotEncoder(28)_handle_unknown | "ignore" |
| sklearn.preprocessing._encoders.OneHotEncoder(28)_n_values | null |
| sklearn.preprocessing._encoders.OneHotEncoder(28)_sparse | true |
| sklearn.feature_selection.variance_threshold.VarianceThreshold(32)_threshold | 0.0 |
| sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=sklearn.preprocessing.data.StandardScaler),nominal=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)),variancethreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold,svc=sklearn.svm.classes.SVC)(2)_memory | null |
| sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=sklearn.preprocessing.data.StandardScaler),nominal=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)),variancethreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold,svc=sklearn.svm.classes.SVC)(2)_steps | [{"oml-python:serialized_object": "component_reference", "value": {"key": "columntransformer", "step_name": "columntransformer"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "variancethreshold", "step_name": "variancethreshold"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "svc", "step_name": "svc"}}] |
| sklearn.svm.classes.SVC(47)_C | 143.11841724167172 |
| sklearn.svm.classes.SVC(47)_cache_size | 200 |
| sklearn.svm.classes.SVC(47)_class_weight | null |
| sklearn.svm.classes.SVC(47)_coef0 | 0.22921525238301776 |
| sklearn.svm.classes.SVC(47)_decision_function_shape | "ovr" |
| sklearn.svm.classes.SVC(47)_degree | 2 |
| sklearn.svm.classes.SVC(47)_gamma | 0.0070192306738709525 |
| sklearn.svm.classes.SVC(47)_kernel | "poly" |
| sklearn.svm.classes.SVC(47)_max_iter | -1 |
| sklearn.svm.classes.SVC(47)_probability | false |
| sklearn.svm.classes.SVC(47)_random_state | 35153 |
| sklearn.svm.classes.SVC(47)_shrinking | false |
| sklearn.svm.classes.SVC(47)_tol | 1.1571247532842242e-05 |
| sklearn.svm.classes.SVC(47)_verbose | false |