Run 4999

Task 115 (Supervised Classification) diabetes Uploaded 15-01-2024 by Continuous Integration
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sklearn.pipeline.Pipeline(scaler=sklearn.preprocessing._data.StandardScaler ,boosting=sklearn.ensemble._weight_boosting.AdaBoostClassifier(base_estimat or=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.pipeline.Pipeline(scaler=sklearn.preprocessing._data.StandardScaler,boosting=sklearn.ensemble._weight_boosting.AdaBoostClassifier(base_estimator=sklearn.tree._classes.DecisionTreeClassifier))(1)_memory	null
sklearn.pipeline.Pipeline(scaler=sklearn.preprocessing._data.StandardScaler,boosting=sklearn.ensemble._weight_boosting.AdaBoostClassifier(base_estimator=sklearn.tree._classes.DecisionTreeClassifier))(1)_steps	[{"oml-python:serialized_object": "component_reference", "value": {"key": "scaler", "step_name": "scaler"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "boosting", "step_name": "boosting"}}]
sklearn.pipeline.Pipeline(scaler=sklearn.preprocessing._data.StandardScaler,boosting=sklearn.ensemble._weight_boosting.AdaBoostClassifier(base_estimator=sklearn.tree._classes.DecisionTreeClassifier))(1)_verbose	false
sklearn.preprocessing._data.StandardScaler(1)_copy	true
sklearn.preprocessing._data.StandardScaler(1)_with_mean	false
sklearn.preprocessing._data.StandardScaler(1)_with_std	true
sklearn.ensemble._weight_boosting.AdaBoostClassifier(base_estimator=sklearn.tree._classes.DecisionTreeClassifier)(1)_algorithm	"SAMME.R"
sklearn.ensemble._weight_boosting.AdaBoostClassifier(base_estimator=sklearn.tree._classes.DecisionTreeClassifier)(1)_estimator	null
sklearn.ensemble._weight_boosting.AdaBoostClassifier(base_estimator=sklearn.tree._classes.DecisionTreeClassifier)(1)_learning_rate	1.0
sklearn.ensemble._weight_boosting.AdaBoostClassifier(base_estimator=sklearn.tree._classes.DecisionTreeClassifier)(1)_n_estimators	50
sklearn.ensemble._weight_boosting.AdaBoostClassifier(base_estimator=sklearn.tree._classes.DecisionTreeClassifier)(1)_random_state	41314
sklearn.tree._classes.DecisionTreeClassifier(1)_ccp_alpha	0.0
sklearn.tree._classes.DecisionTreeClassifier(1)_class_weight	null
sklearn.tree._classes.DecisionTreeClassifier(1)_criterion	"gini"
sklearn.tree._classes.DecisionTreeClassifier(1)_max_depth	null
sklearn.tree._classes.DecisionTreeClassifier(1)_max_features	null
sklearn.tree._classes.DecisionTreeClassifier(1)_max_leaf_nodes	null
sklearn.tree._classes.DecisionTreeClassifier(1)_min_impurity_decrease	0.0
sklearn.tree._classes.DecisionTreeClassifier(1)_min_samples_leaf	1
sklearn.tree._classes.DecisionTreeClassifier(1)_min_samples_split	2
sklearn.tree._classes.DecisionTreeClassifier(1)_min_weight_fraction_leaf	0.0
sklearn.tree._classes.DecisionTreeClassifier(1)_random_state	18095
sklearn.tree._classes.DecisionTreeClassifier(1)_splitter	"best"