Run 10591421

Task 9983 (Supervised Classification) eeg-eye-state Uploaded 12-10-2022 by VAIBHAV JAISWAL
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sklearn.pipeline.Pipeline(numerical=sklearn.pipeline.Pipeline(Imputer=sklea rn.impute._base.SimpleImputer,scaler=sklearn.preprocessing._data.StandardSc aler),model=sklearn.tree._classes.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(11)_copy	true
sklearn.preprocessing._data.StandardScaler(11)_with_mean	true
sklearn.preprocessing._data.StandardScaler(11)_with_std	true
sklearn.impute._base.SimpleImputer(30)_add_indicator	false
sklearn.impute._base.SimpleImputer(30)_copy	true
sklearn.impute._base.SimpleImputer(30)_fill_value	null
sklearn.impute._base.SimpleImputer(30)_missing_values	NaN
sklearn.impute._base.SimpleImputer(30)_strategy	"mean"
sklearn.impute._base.SimpleImputer(30)_verbose	0
sklearn.tree._classes.DecisionTreeClassifier(25)_ccp_alpha	0.0
sklearn.tree._classes.DecisionTreeClassifier(25)_class_weight	null
sklearn.tree._classes.DecisionTreeClassifier(25)_criterion	"gini"
sklearn.tree._classes.DecisionTreeClassifier(25)_max_depth	null
sklearn.tree._classes.DecisionTreeClassifier(25)_max_features	null
sklearn.tree._classes.DecisionTreeClassifier(25)_max_leaf_nodes	null
sklearn.tree._classes.DecisionTreeClassifier(25)_min_impurity_decrease	0.0
sklearn.tree._classes.DecisionTreeClassifier(25)_min_samples_leaf	1
sklearn.tree._classes.DecisionTreeClassifier(25)_min_samples_split	2
sklearn.tree._classes.DecisionTreeClassifier(25)_min_weight_fraction_leaf	0.0
sklearn.tree._classes.DecisionTreeClassifier(25)_random_state	0
sklearn.tree._classes.DecisionTreeClassifier(25)_splitter	"best"
sklearn.pipeline.Pipeline(Imputer=sklearn.impute._base.SimpleImputer,scaler=sklearn.preprocessing._data.StandardScaler)(2)_memory	null
sklearn.pipeline.Pipeline(Imputer=sklearn.impute._base.SimpleImputer,scaler=sklearn.preprocessing._data.StandardScaler)(2)_steps	[{"oml-python:serialized_object": "component_reference", "value": {"key": "Imputer", "step_name": "Imputer"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "scaler", "step_name": "scaler"}}]
sklearn.pipeline.Pipeline(Imputer=sklearn.impute._base.SimpleImputer,scaler=sklearn.preprocessing._data.StandardScaler)(2)_verbose	false
sklearn.pipeline.Pipeline(numerical=sklearn.pipeline.Pipeline(Imputer=sklearn.impute._base.SimpleImputer,scaler=sklearn.preprocessing._data.StandardScaler),model=sklearn.tree._classes.DecisionTreeClassifier)(1)_memory	null
sklearn.pipeline.Pipeline(numerical=sklearn.pipeline.Pipeline(Imputer=sklearn.impute._base.SimpleImputer,scaler=sklearn.preprocessing._data.StandardScaler),model=sklearn.tree._classes.DecisionTreeClassifier)(1)_steps	[{"oml-python:serialized_object": "component_reference", "value": {"key": "numerical", "step_name": "numerical"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "model", "step_name": "model"}}]
sklearn.pipeline.Pipeline(numerical=sklearn.pipeline.Pipeline(Imputer=sklearn.impute._base.SimpleImputer,scaler=sklearn.preprocessing._data.StandardScaler),model=sklearn.tree._classes.DecisionTreeClassifier)(1)_verbose	false