Run 8498

Task 801 (Learning Curve) diabetes Uploaded 25-11-2022 by Continuous Integration
0 likes downloaded by 0 people 0 issues 0 downvotes , 0 total downloads

Issue	#Downvotes for this reason	By

Flow

TESTac735e80b3sklearn.pipeline.Pipeline(Imputer=sklearn.preprocessing.imput ation.Imputer,VarianceThreshold=sklearn.feature_selection.variance_threshol d.VarianceThreshold,Estimator=sklearn.model_selection._search.RandomizedSea rchCV(estimator=sklearn.tree.tree.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 to None.
TESTac735e80b3sklearn.pipeline.Pipeline(Imputer=sklearn.preprocessing.imputation.Imputer,VarianceThreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold,Estimator=sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree.tree.DecisionTreeClassifier))(1)_memory	null
TESTac735e80b3sklearn.pipeline.Pipeline(Imputer=sklearn.preprocessing.imputation.Imputer,VarianceThreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold,Estimator=sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree.tree.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"}}]
TESTac735e80b3sklearn.preprocessing.imputation.Imputer(1)_axis	0
TESTac735e80b3sklearn.preprocessing.imputation.Imputer(1)_copy	true
TESTac735e80b3sklearn.preprocessing.imputation.Imputer(1)_missing_values	"NaN"
TESTac735e80b3sklearn.preprocessing.imputation.Imputer(1)_strategy	"median"
TESTac735e80b3sklearn.preprocessing.imputation.Imputer(1)_verbose	0
TESTac735e80b3sklearn.feature_selection.variance_threshold.VarianceThreshold(1)_threshold	0.0
TESTac735e80b3sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree.tree.DecisionTreeClassifier)(1)_cv	3
TESTac735e80b3sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree.tree.DecisionTreeClassifier)(1)_error_score	"raise"
TESTac735e80b3sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree.tree.DecisionTreeClassifier)(1)_fit_params	null
TESTac735e80b3sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree.tree.DecisionTreeClassifier)(1)_iid	true
TESTac735e80b3sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree.tree.DecisionTreeClassifier)(1)_n_iter	10
TESTac735e80b3sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree.tree.DecisionTreeClassifier)(1)_n_jobs	1
TESTac735e80b3sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree.tree.DecisionTreeClassifier)(1)_param_distributions	{"min_samples_leaf": [1, 2, 4, 8, 16, 32, 64], "min_samples_split": [2, 4, 8, 16, 32, 64, 128]}
TESTac735e80b3sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree.tree.DecisionTreeClassifier)(1)_pre_dispatch	"2*n_jobs"
TESTac735e80b3sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree.tree.DecisionTreeClassifier)(1)_random_state	33003
TESTac735e80b3sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree.tree.DecisionTreeClassifier)(1)_refit	true
TESTac735e80b3sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree.tree.DecisionTreeClassifier)(1)_return_train_score	"warn"
TESTac735e80b3sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree.tree.DecisionTreeClassifier)(1)_scoring	null
TESTac735e80b3sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree.tree.DecisionTreeClassifier)(1)_verbose	0
TESTac735e80b3sklearn.tree.tree.DecisionTreeClassifier(1)_class_weight	null
TESTac735e80b3sklearn.tree.tree.DecisionTreeClassifier(1)_criterion	"gini"
TESTac735e80b3sklearn.tree.tree.DecisionTreeClassifier(1)_max_depth	null
TESTac735e80b3sklearn.tree.tree.DecisionTreeClassifier(1)_max_features	null
TESTac735e80b3sklearn.tree.tree.DecisionTreeClassifier(1)_max_leaf_nodes	null
TESTac735e80b3sklearn.tree.tree.DecisionTreeClassifier(1)_min_impurity_decrease	0.0
TESTac735e80b3sklearn.tree.tree.DecisionTreeClassifier(1)_min_impurity_split	null
TESTac735e80b3sklearn.tree.tree.DecisionTreeClassifier(1)_min_samples_leaf	1
TESTac735e80b3sklearn.tree.tree.DecisionTreeClassifier(1)_min_samples_split	2
TESTac735e80b3sklearn.tree.tree.DecisionTreeClassifier(1)_min_weight_fraction_leaf	0.0
TESTac735e80b3sklearn.tree.tree.DecisionTreeClassifier(1)_presort	false
TESTac735e80b3sklearn.tree.tree.DecisionTreeClassifier(1)_random_state	62501
TESTac735e80b3sklearn.tree.tree.DecisionTreeClassifier(1)_splitter	"best"