| sklearn.pipeline.Pipeline(Preprocessing=sklearn.compose._column_transformer
.ColumnTransformer(categorical=sklearn.pipeline.Pipeline(onehotencoder=skle
arn.preprocessing._encoders.OneHotEncoder,simpleimputer=sklearn.impute._bas
e.SimpleImputer),numeric=sklearn.pipeline.Pipeline(variancethreshold=sklear
n.feature_selection._variance_threshold.VarianceThreshold,standardscaler=sk
learn.preprocessing._data.StandardScaler,knnimputer=sklearn.impute._knn.KNN
Imputer)),BayesinClassifier=sklearn.svm._classes.SVC)(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.compose._column_transformer.ColumnTransformer(categorical=sklearn.pipeline.Pipeline(onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder,simpleimputer=sklearn.impute._base.SimpleImputer),numeric=sklearn.pipeline.Pipeline(variancethreshold=sklearn.feature_selection._variance_threshold.VarianceThreshold,standardscaler=sklearn.preprocessing._data.StandardScaler,knnimputer=sklearn.impute._knn.KNNImputer))(2)_n_jobs | null |
| sklearn.compose._column_transformer.ColumnTransformer(categorical=sklearn.pipeline.Pipeline(onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder,simpleimputer=sklearn.impute._base.SimpleImputer),numeric=sklearn.pipeline.Pipeline(variancethreshold=sklearn.feature_selection._variance_threshold.VarianceThreshold,standardscaler=sklearn.preprocessing._data.StandardScaler,knnimputer=sklearn.impute._knn.KNNImputer))(2)_remainder | "drop" |
| sklearn.compose._column_transformer.ColumnTransformer(categorical=sklearn.pipeline.Pipeline(onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder,simpleimputer=sklearn.impute._base.SimpleImputer),numeric=sklearn.pipeline.Pipeline(variancethreshold=sklearn.feature_selection._variance_threshold.VarianceThreshold,standardscaler=sklearn.preprocessing._data.StandardScaler,knnimputer=sklearn.impute._knn.KNNImputer))(2)_sparse_threshold | 0.3 |
| sklearn.compose._column_transformer.ColumnTransformer(categorical=sklearn.pipeline.Pipeline(onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder,simpleimputer=sklearn.impute._base.SimpleImputer),numeric=sklearn.pipeline.Pipeline(variancethreshold=sklearn.feature_selection._variance_threshold.VarianceThreshold,standardscaler=sklearn.preprocessing._data.StandardScaler,knnimputer=sklearn.impute._knn.KNNImputer))(2)_transformer_weights | null |
| sklearn.compose._column_transformer.ColumnTransformer(categorical=sklearn.pipeline.Pipeline(onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder,simpleimputer=sklearn.impute._base.SimpleImputer),numeric=sklearn.pipeline.Pipeline(variancethreshold=sklearn.feature_selection._variance_threshold.VarianceThreshold,standardscaler=sklearn.preprocessing._data.StandardScaler,knnimputer=sklearn.impute._knn.KNNImputer))(2)_transformers | [{"oml-python:serialized_object": "component_reference", "value": {"key": "categorical", "step_name": "categorical", "argument_1": {"oml-python:serialized_object": "function", "value": "openml.extensions.sklearn.cat"}}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "numeric", "step_name": "numeric", "argument_1": {"oml-python:serialized_object": "function", "value": "openml.extensions.sklearn.cont"}}}] |
| sklearn.compose._column_transformer.ColumnTransformer(categorical=sklearn.pipeline.Pipeline(onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder,simpleimputer=sklearn.impute._base.SimpleImputer),numeric=sklearn.pipeline.Pipeline(variancethreshold=sklearn.feature_selection._variance_threshold.VarianceThreshold,standardscaler=sklearn.preprocessing._data.StandardScaler,knnimputer=sklearn.impute._knn.KNNImputer))(2)_verbose | false |
| sklearn.compose._column_transformer.ColumnTransformer(categorical=sklearn.pipeline.Pipeline(onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder,simpleimputer=sklearn.impute._base.SimpleImputer),numeric=sklearn.pipeline.Pipeline(variancethreshold=sklearn.feature_selection._variance_threshold.VarianceThreshold,standardscaler=sklearn.preprocessing._data.StandardScaler,knnimputer=sklearn.impute._knn.KNNImputer))(2)_verbose_feature_names_out | true |
| sklearn.pipeline.Pipeline(onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder,simpleimputer=sklearn.impute._base.SimpleImputer)(2)_memory | null |
| sklearn.pipeline.Pipeline(onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder,simpleimputer=sklearn.impute._base.SimpleImputer)(2)_steps | [{"oml-python:serialized_object": "component_reference", "value": {"key": "onehotencoder", "step_name": "onehotencoder"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "simpleimputer", "step_name": "simpleimputer"}}] |
| sklearn.pipeline.Pipeline(onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder,simpleimputer=sklearn.impute._base.SimpleImputer)(2)_verbose | false |
| sklearn.preprocessing._encoders.OneHotEncoder(44)_categories | "auto" |
| sklearn.preprocessing._encoders.OneHotEncoder(44)_drop | null |
| sklearn.preprocessing._encoders.OneHotEncoder(44)_dtype | {"oml-python:serialized_object": "type", "value": "np.float64"} |
| sklearn.preprocessing._encoders.OneHotEncoder(44)_feature_name_combiner | "concat" |
| sklearn.preprocessing._encoders.OneHotEncoder(44)_handle_unknown | "infrequent_if_exist" |
| sklearn.preprocessing._encoders.OneHotEncoder(44)_max_categories | null |
| sklearn.preprocessing._encoders.OneHotEncoder(44)_min_frequency | null |
| sklearn.preprocessing._encoders.OneHotEncoder(44)_sparse | false |
| sklearn.preprocessing._encoders.OneHotEncoder(44)_sparse_output | true |
| sklearn.impute._base.SimpleImputer(48)_add_indicator | false |
| sklearn.impute._base.SimpleImputer(48)_copy | true |
| sklearn.impute._base.SimpleImputer(48)_fill_value | null |
| sklearn.impute._base.SimpleImputer(48)_keep_empty_features | false |
| sklearn.impute._base.SimpleImputer(48)_missing_values | NaN |
| sklearn.impute._base.SimpleImputer(48)_strategy | "most_frequent" |
| sklearn.pipeline.Pipeline(variancethreshold=sklearn.feature_selection._variance_threshold.VarianceThreshold,standardscaler=sklearn.preprocessing._data.StandardScaler,knnimputer=sklearn.impute._knn.KNNImputer)(2)_memory | null |
| sklearn.pipeline.Pipeline(variancethreshold=sklearn.feature_selection._variance_threshold.VarianceThreshold,standardscaler=sklearn.preprocessing._data.StandardScaler,knnimputer=sklearn.impute._knn.KNNImputer)(2)_steps | [{"oml-python:serialized_object": "component_reference", "value": {"key": "variancethreshold", "step_name": "variancethreshold"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "standardscaler", "step_name": "standardscaler"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "knnimputer", "step_name": "knnimputer"}}] |
| sklearn.pipeline.Pipeline(variancethreshold=sklearn.feature_selection._variance_threshold.VarianceThreshold,standardscaler=sklearn.preprocessing._data.StandardScaler,knnimputer=sklearn.impute._knn.KNNImputer)(2)_verbose | false |
| sklearn.feature_selection._variance_threshold.VarianceThreshold(10)_threshold | 0.0 |
| sklearn.preprocessing._data.StandardScaler(16)_copy | true |
| sklearn.preprocessing._data.StandardScaler(16)_with_mean | true |
| sklearn.preprocessing._data.StandardScaler(16)_with_std | true |
| sklearn.impute._knn.KNNImputer(3)_add_indicator | false |
| sklearn.impute._knn.KNNImputer(3)_copy | true |
| sklearn.impute._knn.KNNImputer(3)_keep_empty_features | false |
| sklearn.impute._knn.KNNImputer(3)_metric | "nan_euclidean" |
| sklearn.impute._knn.KNNImputer(3)_missing_values | NaN |
| sklearn.impute._knn.KNNImputer(3)_n_neighbors | 5 |
| sklearn.impute._knn.KNNImputer(3)_weights | "uniform" |
| sklearn.pipeline.Pipeline(Preprocessing=sklearn.compose._column_transformer.ColumnTransformer(categorical=sklearn.pipeline.Pipeline(onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder,simpleimputer=sklearn.impute._base.SimpleImputer),numeric=sklearn.pipeline.Pipeline(variancethreshold=sklearn.feature_selection._variance_threshold.VarianceThreshold,standardscaler=sklearn.preprocessing._data.StandardScaler,knnimputer=sklearn.impute._knn.KNNImputer)),BayesinClassifier=sklearn.svm._classes.SVC)(1)_memory | null |
| sklearn.pipeline.Pipeline(Preprocessing=sklearn.compose._column_transformer.ColumnTransformer(categorical=sklearn.pipeline.Pipeline(onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder,simpleimputer=sklearn.impute._base.SimpleImputer),numeric=sklearn.pipeline.Pipeline(variancethreshold=sklearn.feature_selection._variance_threshold.VarianceThreshold,standardscaler=sklearn.preprocessing._data.StandardScaler,knnimputer=sklearn.impute._knn.KNNImputer)),BayesinClassifier=sklearn.svm._classes.SVC)(1)_steps | [{"oml-python:serialized_object": "component_reference", "value": {"key": "Preprocessing", "step_name": "Preprocessing"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "BayesinClassifier", "step_name": "BayesinClassifier"}}] |
| sklearn.pipeline.Pipeline(Preprocessing=sklearn.compose._column_transformer.ColumnTransformer(categorical=sklearn.pipeline.Pipeline(onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder,simpleimputer=sklearn.impute._base.SimpleImputer),numeric=sklearn.pipeline.Pipeline(variancethreshold=sklearn.feature_selection._variance_threshold.VarianceThreshold,standardscaler=sklearn.preprocessing._data.StandardScaler,knnimputer=sklearn.impute._knn.KNNImputer)),BayesinClassifier=sklearn.svm._classes.SVC)(1)_verbose | false |
| sklearn.svm._classes.SVC(16)_C | 0.8122664973899175 |
| sklearn.svm._classes.SVC(16)_break_ties | false |
| sklearn.svm._classes.SVC(16)_cache_size | 200 |
| sklearn.svm._classes.SVC(16)_class_weight | null |
| sklearn.svm._classes.SVC(16)_coef0 | 1.1490926423856163 |
| sklearn.svm._classes.SVC(16)_decision_function_shape | "ovr" |
| sklearn.svm._classes.SVC(16)_degree | 2 |
| sklearn.svm._classes.SVC(16)_gamma | "auto" |
| sklearn.svm._classes.SVC(16)_kernel | "rbf" |
| sklearn.svm._classes.SVC(16)_max_iter | -1 |
| sklearn.svm._classes.SVC(16)_probability | true |
| sklearn.svm._classes.SVC(16)_random_state | 36282 |
| sklearn.svm._classes.SVC(16)_shrinking | true |
| sklearn.svm._classes.SVC(16)_tol | 0.001 |
| sklearn.svm._classes.SVC(16)_verbose | false |