Run 10560774

Task 146825 (Supervised Classification) Fashion-MNIST Uploaded 26-08-2021 by Pieter Gijsbers
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sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transfo rmer.ColumnTransformer(simpleimputer=sklearn.impute._base.SimpleImputer,one hotencoder=sklearn.preprocessing._encoders.OneHotEncoder),decisiontreeclass ifier=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.impute._base.SimpleImputer(25)_add_indicator	false
sklearn.impute._base.SimpleImputer(25)_copy	true
sklearn.impute._base.SimpleImputer(25)_fill_value	null
sklearn.impute._base.SimpleImputer(25)_missing_values	NaN
sklearn.impute._base.SimpleImputer(25)_strategy	"mean"
sklearn.impute._base.SimpleImputer(25)_verbose	0
sklearn.tree._classes.DecisionTreeClassifier(20)_ccp_alpha	0.0
sklearn.tree._classes.DecisionTreeClassifier(20)_class_weight	null
sklearn.tree._classes.DecisionTreeClassifier(20)_criterion	"gini"
sklearn.tree._classes.DecisionTreeClassifier(20)_max_depth	1
sklearn.tree._classes.DecisionTreeClassifier(20)_max_features	null
sklearn.tree._classes.DecisionTreeClassifier(20)_max_leaf_nodes	null
sklearn.tree._classes.DecisionTreeClassifier(20)_min_impurity_decrease	0.0
sklearn.tree._classes.DecisionTreeClassifier(20)_min_impurity_split	null
sklearn.tree._classes.DecisionTreeClassifier(20)_min_samples_leaf	1
sklearn.tree._classes.DecisionTreeClassifier(20)_min_samples_split	2
sklearn.tree._classes.DecisionTreeClassifier(20)_min_weight_fraction_leaf	0.0
sklearn.tree._classes.DecisionTreeClassifier(20)_random_state	31861
sklearn.tree._classes.DecisionTreeClassifier(20)_splitter	"best"
sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder),decisiontreeclassifier=sklearn.tree._classes.DecisionTreeClassifier)(1)_memory	null
sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder),decisiontreeclassifier=sklearn.tree._classes.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(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder),decisiontreeclassifier=sklearn.tree._classes.DecisionTreeClassifier)(1)_verbose	false
sklearn.compose._column_transformer.ColumnTransformer(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)(1)_n_jobs	null
sklearn.compose._column_transformer.ColumnTransformer(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)(1)_remainder	"drop"
sklearn.compose._column_transformer.ColumnTransformer(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)(1)_sparse_threshold	0.3
sklearn.compose._column_transformer.ColumnTransformer(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)(1)_transformer_weights	null
sklearn.compose._column_transformer.ColumnTransformer(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)(1)_transformers	[{"oml-python:serialized_object": "component_reference", "value": {"key": "simpleimputer", "step_name": "simpleimputer", "argument_1": {"oml-python:serialized_object": "function", "value": "openml.extensions.sklearn.cont"}}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "onehotencoder", "step_name": "onehotencoder", "argument_1": {"oml-python:serialized_object": "function", "value": "openml.extensions.sklearn.cat"}}}]
sklearn.compose._column_transformer.ColumnTransformer(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)(1)_verbose	false
sklearn.preprocessing._encoders.OneHotEncoder(29)_categories	"auto"
sklearn.preprocessing._encoders.OneHotEncoder(29)_drop	null
sklearn.preprocessing._encoders.OneHotEncoder(29)_dtype	{"oml-python:serialized_object": "type", "value": "np.float64"}
sklearn.preprocessing._encoders.OneHotEncoder(29)_handle_unknown	"ignore"
sklearn.preprocessing._encoders.OneHotEncoder(29)_sparse	true