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Task 115 (Supervised Classification) diabetes Uploaded 17-10-2024 by Continuous Integration
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sklearn.pipeline.Pipeline(Imputer=sklearn.impute._base.SimpleImputer,Varian ceThreshold=sklearn.feature_selection._variance_threshold.VarianceThreshold ,Estimator=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`. For an example use case of `Pipeline` combined with :class:`~sklearn.model_selection.GridSearchCV`, refer to :ref:`sphx_glr_auto_examples_compose_plot_compare_reduction.py`. The example :ref:`sphx_glr_auto_exampl...
sklearn.tree._classes.DecisionTreeClassifier(3)_ccp_alpha	0.0
sklearn.tree._classes.DecisionTreeClassifier(3)_class_weight	null
sklearn.tree._classes.DecisionTreeClassifier(3)_criterion	"gini"
sklearn.tree._classes.DecisionTreeClassifier(3)_max_depth	4
sklearn.tree._classes.DecisionTreeClassifier(3)_max_features	null
sklearn.tree._classes.DecisionTreeClassifier(3)_max_leaf_nodes	null
sklearn.tree._classes.DecisionTreeClassifier(3)_min_impurity_decrease	0.0
sklearn.tree._classes.DecisionTreeClassifier(3)_min_samples_leaf	1
sklearn.tree._classes.DecisionTreeClassifier(3)_min_samples_split	2
sklearn.tree._classes.DecisionTreeClassifier(3)_min_weight_fraction_leaf	0.0
sklearn.tree._classes.DecisionTreeClassifier(3)_random_state	62501
sklearn.tree._classes.DecisionTreeClassifier(3)_splitter	"best"
sklearn.impute._base.SimpleImputer(1)_add_indicator	false
sklearn.impute._base.SimpleImputer(1)_copy	true
sklearn.impute._base.SimpleImputer(1)_fill_value	null
sklearn.impute._base.SimpleImputer(1)_keep_empty_features	false
sklearn.impute._base.SimpleImputer(1)_missing_values	NaN
sklearn.impute._base.SimpleImputer(1)_strategy	"mean"
sklearn.pipeline.Pipeline(Imputer=sklearn.impute._base.SimpleImputer,VarianceThreshold=sklearn.feature_selection._variance_threshold.VarianceThreshold,Estimator=sklearn.tree._classes.DecisionTreeClassifier)(1)_memory	null
sklearn.pipeline.Pipeline(Imputer=sklearn.impute._base.SimpleImputer,VarianceThreshold=sklearn.feature_selection._variance_threshold.VarianceThreshold,Estimator=sklearn.tree._classes.DecisionTreeClassifier)(1)_steps	[{"oml-python:serialized_object": "component_reference", "value": {"key": "Imputer", "step_name": "Imputer"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "VarianceThreshold", "step_name": "VarianceThreshold"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "Estimator", "step_name": "Estimator"}}]
sklearn.pipeline.Pipeline(Imputer=sklearn.impute._base.SimpleImputer,VarianceThreshold=sklearn.feature_selection._variance_threshold.VarianceThreshold,Estimator=sklearn.tree._classes.DecisionTreeClassifier)(1)_verbose	false
sklearn.feature_selection._variance_threshold.VarianceThreshold(1)_threshold	0.05