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Task 119 (Supervised Classification) diabetes Uploaded 17-10-2024 by Continuous Integration
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sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,classi fier=sklearn.tree._classes.DecisionTreeClassifier)(7)	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.tree._classes.DecisionTreeClassifier(8)_ccp_alpha	0.0
sklearn.tree._classes.DecisionTreeClassifier(8)_class_weight	null
sklearn.tree._classes.DecisionTreeClassifier(8)_criterion	"gini"
sklearn.tree._classes.DecisionTreeClassifier(8)_max_depth	1
sklearn.tree._classes.DecisionTreeClassifier(8)_max_features	null
sklearn.tree._classes.DecisionTreeClassifier(8)_max_leaf_nodes	null
sklearn.tree._classes.DecisionTreeClassifier(8)_min_impurity_decrease	0.0
sklearn.tree._classes.DecisionTreeClassifier(8)_min_samples_leaf	1
sklearn.tree._classes.DecisionTreeClassifier(8)_min_samples_split	2
sklearn.tree._classes.DecisionTreeClassifier(8)_min_weight_fraction_leaf	0.0
sklearn.tree._classes.DecisionTreeClassifier(8)_random_state	43715
sklearn.tree._classes.DecisionTreeClassifier(8)_splitter	"best"
sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,classifier=sklearn.tree._classes.DecisionTreeClassifier)(7)_memory	null
sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,classifier=sklearn.tree._classes.DecisionTreeClassifier)(7)_steps	[{"oml-python:serialized_object": "component_reference", "value": {"key": "imputer", "step_name": "imputer"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "classifier", "step_name": "classifier"}}]
sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,classifier=sklearn.tree._classes.DecisionTreeClassifier)(7)_verbose	false
sklearn.impute._base.SimpleImputer(7)_add_indicator	false
sklearn.impute._base.SimpleImputer(7)_copy	true
sklearn.impute._base.SimpleImputer(7)_fill_value	null
sklearn.impute._base.SimpleImputer(7)_keep_empty_features	false
sklearn.impute._base.SimpleImputer(7)_missing_values	NaN
sklearn.impute._base.SimpleImputer(7)_strategy	"mean"
sklearn.impute._base.SimpleImputer(7)_verbose	"deprecated"