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Task 115 (Supervised Classification) diabetes Uploaded 04-07-2024 by Continuous Integration
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sklearn.pipeline.Pipeline(scaler=sklearn.preprocessing._data.StandardScaler ,boosting=sklearn.ensemble._weight_boosting.AdaBoostClassifier(estimator=sk learn.tree._classes.DecisionTreeClassifier))(3)	A sequence of data transformers with an optional final predictor. `Pipeline` allows you to sequentially apply a list of transformers to preprocess the data and, if desired, conclude the sequence with a final :term:`predictor` for predictive modeling. Intermediate steps of the pipeline must be 'transforms', that is, they must implement `fit` and `transform` methods. The final :term:`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:`~s...
sklearn.preprocessing._data.StandardScaler(6)_copy	true
sklearn.preprocessing._data.StandardScaler(6)_with_mean	false
sklearn.preprocessing._data.StandardScaler(6)_with_std	true
sklearn.pipeline.Pipeline(scaler=sklearn.preprocessing._data.StandardScaler,boosting=sklearn.ensemble._weight_boosting.AdaBoostClassifier(estimator=sklearn.tree._classes.DecisionTreeClassifier))(3)_memory	null
sklearn.pipeline.Pipeline(scaler=sklearn.preprocessing._data.StandardScaler,boosting=sklearn.ensemble._weight_boosting.AdaBoostClassifier(estimator=sklearn.tree._classes.DecisionTreeClassifier))(3)_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(estimator=sklearn.tree._classes.DecisionTreeClassifier))(3)_verbose	false
sklearn.ensemble._weight_boosting.AdaBoostClassifier(estimator=sklearn.tree._classes.DecisionTreeClassifier)(3)_algorithm	"SAMME.R"
sklearn.ensemble._weight_boosting.AdaBoostClassifier(estimator=sklearn.tree._classes.DecisionTreeClassifier)(3)_learning_rate	1.0
sklearn.ensemble._weight_boosting.AdaBoostClassifier(estimator=sklearn.tree._classes.DecisionTreeClassifier)(3)_n_estimators	50
sklearn.ensemble._weight_boosting.AdaBoostClassifier(estimator=sklearn.tree._classes.DecisionTreeClassifier)(3)_random_state	50880
sklearn.tree._classes.DecisionTreeClassifier(11)_ccp_alpha	0.0
sklearn.tree._classes.DecisionTreeClassifier(11)_class_weight	null
sklearn.tree._classes.DecisionTreeClassifier(11)_criterion	"gini"
sklearn.tree._classes.DecisionTreeClassifier(11)_max_depth	null
sklearn.tree._classes.DecisionTreeClassifier(11)_max_features	null
sklearn.tree._classes.DecisionTreeClassifier(11)_max_leaf_nodes	null
sklearn.tree._classes.DecisionTreeClassifier(11)_min_impurity_decrease	0.0
sklearn.tree._classes.DecisionTreeClassifier(11)_min_samples_leaf	1
sklearn.tree._classes.DecisionTreeClassifier(11)_min_samples_split	2
sklearn.tree._classes.DecisionTreeClassifier(11)_min_weight_fraction_leaf	0.0
sklearn.tree._classes.DecisionTreeClassifier(11)_monotonic_cst	null
sklearn.tree._classes.DecisionTreeClassifier(11)_random_state	56221
sklearn.tree._classes.DecisionTreeClassifier(11)_splitter	"best"