| sklearn.pipeline.Pipeline(imputation=openmlstudy14.preprocessing.Conditiona
lImputer,hotencoding=sklearn.compose._column_transformer.ColumnTransformer(
enc=sklearn.preprocessing._encoders.OneHotEncoder),scaling=sklearn.preproce
ssing._data.StandardScaler,variencethreshold=sklearn.feature_selection._var
iance_threshold.VarianceThreshold,classifier=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.pipeline.Pipeline(imputation=openmlstudy14.preprocessing.ConditionalImputer,hotencoding=sklearn.compose._column_transformer.ColumnTransformer(enc=sklearn.preprocessing._encoders.OneHotEncoder),scaling=sklearn.preprocessing._data.StandardScaler,variencethreshold=sklearn.feature_selection._variance_threshold.VarianceThreshold,classifier=sklearn.svm._classes.SVC)(1)_memory | null |
| sklearn.pipeline.Pipeline(imputation=openmlstudy14.preprocessing.ConditionalImputer,hotencoding=sklearn.compose._column_transformer.ColumnTransformer(enc=sklearn.preprocessing._encoders.OneHotEncoder),scaling=sklearn.preprocessing._data.StandardScaler,variencethreshold=sklearn.feature_selection._variance_threshold.VarianceThreshold,classifier=sklearn.svm._classes.SVC)(1)_steps | [{"oml-python:serialized_object": "component_reference", "value": {"key": "imputation", "step_name": "imputation"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "hotencoding", "step_name": "hotencoding"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "scaling", "step_name": "scaling"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "variencethreshold", "step_name": "variencethreshold"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "classifier", "step_name": "classifier"}}] |
| sklearn.pipeline.Pipeline(imputation=openmlstudy14.preprocessing.ConditionalImputer,hotencoding=sklearn.compose._column_transformer.ColumnTransformer(enc=sklearn.preprocessing._encoders.OneHotEncoder),scaling=sklearn.preprocessing._data.StandardScaler,variencethreshold=sklearn.feature_selection._variance_threshold.VarianceThreshold,classifier=sklearn.svm._classes.SVC)(1)_verbose | false |
| openmlstudy14.preprocessing.ConditionalImputer(10)_add_indicator | false |
| openmlstudy14.preprocessing.ConditionalImputer(10)_axis | 0 |
| openmlstudy14.preprocessing.ConditionalImputer(10)_categorical_features | [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35] |
| openmlstudy14.preprocessing.ConditionalImputer(10)_copy | true |
| openmlstudy14.preprocessing.ConditionalImputer(10)_fill_empty | 0 |
| openmlstudy14.preprocessing.ConditionalImputer(10)_missing_values | NaN |
| openmlstudy14.preprocessing.ConditionalImputer(10)_strategy | "mean" |
| openmlstudy14.preprocessing.ConditionalImputer(10)_strategy_nominal | "most_frequent" |
| openmlstudy14.preprocessing.ConditionalImputer(10)_verbose | 0 |
| sklearn.compose._column_transformer.ColumnTransformer(enc=sklearn.preprocessing._encoders.OneHotEncoder)(2)_n_jobs | null |
| sklearn.compose._column_transformer.ColumnTransformer(enc=sklearn.preprocessing._encoders.OneHotEncoder)(2)_remainder | "passthrough" |
| sklearn.compose._column_transformer.ColumnTransformer(enc=sklearn.preprocessing._encoders.OneHotEncoder)(2)_sparse_threshold | 0.3 |
| sklearn.compose._column_transformer.ColumnTransformer(enc=sklearn.preprocessing._encoders.OneHotEncoder)(2)_transformer_weights | null |
| sklearn.compose._column_transformer.ColumnTransformer(enc=sklearn.preprocessing._encoders.OneHotEncoder)(2)_transformers | [{"oml-python:serialized_object": "component_reference", "value": {"key": "enc", "step_name": "enc", "argument_1": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35]}}] |
| sklearn.compose._column_transformer.ColumnTransformer(enc=sklearn.preprocessing._encoders.OneHotEncoder)(2)_verbose | false |
| sklearn.preprocessing._encoders.OneHotEncoder(26)_categories | "auto" |
| sklearn.preprocessing._encoders.OneHotEncoder(26)_drop | null |
| sklearn.preprocessing._encoders.OneHotEncoder(26)_dtype | {"oml-python:serialized_object": "type", "value": "np.float64"} |
| sklearn.preprocessing._encoders.OneHotEncoder(26)_handle_unknown | "ignore" |
| sklearn.preprocessing._encoders.OneHotEncoder(26)_sparse | false |
| sklearn.preprocessing._data.StandardScaler(7)_copy | true |
| sklearn.preprocessing._data.StandardScaler(7)_with_mean | false |
| sklearn.preprocessing._data.StandardScaler(7)_with_std | true |
| sklearn.feature_selection._variance_threshold.VarianceThreshold(4)_threshold | 0.0 |
| sklearn.svm._classes.SVC(8)_C | 0.559956366390858 |
| sklearn.svm._classes.SVC(8)_break_ties | false |
| sklearn.svm._classes.SVC(8)_cache_size | 200 |
| sklearn.svm._classes.SVC(8)_class_weight | null |
| sklearn.svm._classes.SVC(8)_coef0 | 0.12024401645987903 |
| sklearn.svm._classes.SVC(8)_decision_function_shape | "ovr" |
| sklearn.svm._classes.SVC(8)_degree | 3 |
| sklearn.svm._classes.SVC(8)_gamma | 0.0004899581039164595 |
| sklearn.svm._classes.SVC(8)_kernel | "sigmoid" |
| sklearn.svm._classes.SVC(8)_max_iter | -1 |
| sklearn.svm._classes.SVC(8)_probability | false |
| sklearn.svm._classes.SVC(8)_random_state | 21616 |
| sklearn.svm._classes.SVC(8)_shrinking | false |
| sklearn.svm._classes.SVC(8)_tol | 0.00015812804979084444 |
| sklearn.svm._classes.SVC(8)_verbose | false |