Run 2224

Task 115 (Supervised Classification) diabetes Uploaded 17-10-2024 by Continuous Integration
0 likes downloaded by 0 people 0 issues 0 downvotes , 0 total downloads

Issue	#Downvotes for this reason	By

Flow

sklearn.pipeline.Pipeline(scaler=sklearn.preprocessing._data.StandardScaler ,boosting=sklearn.ensemble._weight_boosting.AdaBoostClassifier(estimator=sk learn.tree._classes.DecisionTreeClassifier))(2)	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.pipeline.Pipeline(scaler=sklearn.preprocessing._data.StandardScaler,boosting=sklearn.ensemble._weight_boosting.AdaBoostClassifier(estimator=sklearn.tree._classes.DecisionTreeClassifier))(2)_memory	null
sklearn.pipeline.Pipeline(scaler=sklearn.preprocessing._data.StandardScaler,boosting=sklearn.ensemble._weight_boosting.AdaBoostClassifier(estimator=sklearn.tree._classes.DecisionTreeClassifier))(2)_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))(2)_verbose	false
sklearn.preprocessing._data.StandardScaler(7)_copy	true
sklearn.preprocessing._data.StandardScaler(7)_with_mean	false
sklearn.preprocessing._data.StandardScaler(7)_with_std	true
sklearn.ensemble._weight_boosting.AdaBoostClassifier(estimator=sklearn.tree._classes.DecisionTreeClassifier)(2)_algorithm	"SAMME.R"
sklearn.ensemble._weight_boosting.AdaBoostClassifier(estimator=sklearn.tree._classes.DecisionTreeClassifier)(2)_learning_rate	1.0
sklearn.ensemble._weight_boosting.AdaBoostClassifier(estimator=sklearn.tree._classes.DecisionTreeClassifier)(2)_n_estimators	50
sklearn.ensemble._weight_boosting.AdaBoostClassifier(estimator=sklearn.tree._classes.DecisionTreeClassifier)(2)_random_state	49429
sklearn.tree._classes.DecisionTreeClassifier(7)_ccp_alpha	0.0
sklearn.tree._classes.DecisionTreeClassifier(7)_class_weight	null
sklearn.tree._classes.DecisionTreeClassifier(7)_criterion	"gini"
sklearn.tree._classes.DecisionTreeClassifier(7)_max_depth	null
sklearn.tree._classes.DecisionTreeClassifier(7)_max_features	null
sklearn.tree._classes.DecisionTreeClassifier(7)_max_leaf_nodes	null
sklearn.tree._classes.DecisionTreeClassifier(7)_min_impurity_decrease	0.0
sklearn.tree._classes.DecisionTreeClassifier(7)_min_samples_leaf	1
sklearn.tree._classes.DecisionTreeClassifier(7)_min_samples_split	2
sklearn.tree._classes.DecisionTreeClassifier(7)_min_weight_fraction_leaf	0.0
sklearn.tree._classes.DecisionTreeClassifier(7)_monotonic_cst	null
sklearn.tree._classes.DecisionTreeClassifier(7)_random_state	60115
sklearn.tree._classes.DecisionTreeClassifier(7)_splitter	"best"