Run 4642

Task 801 (Learning Curve) diabetes Uploaded 18-10-2024 by Continuous Integration
0 likes downloaded by 0 people 0 issues 0 downvotes , 0 total downloads

Issue	#Downvotes for this reason	By

Flow

TESTf4e82a6525sklearn.pipeline.Pipeline(Imputer=sklearn.impute._base.Simple Imputer,VarianceThreshold=sklearn.feature_selection._variance_threshold.Var ianceThreshold,Estimator=sklearn.model_selection._search.RandomizedSearchCV (estimator=sklearn.tree._classes.DecisionTreeClassifier))(1)	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...
TESTf4e82a6525sklearn.pipeline.Pipeline(Imputer=sklearn.impute._base.SimpleImputer,VarianceThreshold=sklearn.feature_selection._variance_threshold.VarianceThreshold,Estimator=sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree._classes.DecisionTreeClassifier))(1)_memory	null
TESTf4e82a6525sklearn.pipeline.Pipeline(Imputer=sklearn.impute._base.SimpleImputer,VarianceThreshold=sklearn.feature_selection._variance_threshold.VarianceThreshold,Estimator=sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree._classes.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"}}]
TESTf4e82a6525sklearn.pipeline.Pipeline(Imputer=sklearn.impute._base.SimpleImputer,VarianceThreshold=sklearn.feature_selection._variance_threshold.VarianceThreshold,Estimator=sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree._classes.DecisionTreeClassifier))(1)_verbose	false
TESTf4e82a6525sklearn.impute._base.SimpleImputer(1)_add_indicator	false
TESTf4e82a6525sklearn.impute._base.SimpleImputer(1)_copy	true
TESTf4e82a6525sklearn.impute._base.SimpleImputer(1)_fill_value	null
TESTf4e82a6525sklearn.impute._base.SimpleImputer(1)_keep_empty_features	false
TESTf4e82a6525sklearn.impute._base.SimpleImputer(1)_missing_values	NaN
TESTf4e82a6525sklearn.impute._base.SimpleImputer(1)_strategy	"median"
TESTf4e82a6525sklearn.feature_selection._variance_threshold.VarianceThreshold(1)_threshold	0.0
TESTf4e82a6525sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree._classes.DecisionTreeClassifier)(1)_cv	3
TESTf4e82a6525sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree._classes.DecisionTreeClassifier)(1)_error_score	NaN
TESTf4e82a6525sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree._classes.DecisionTreeClassifier)(1)_n_iter	10
TESTf4e82a6525sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree._classes.DecisionTreeClassifier)(1)_n_jobs	null
TESTf4e82a6525sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree._classes.DecisionTreeClassifier)(1)_param_distributions	{"min_samples_leaf": [1, 2, 4, 8, 16, 32, 64], "min_samples_split": [2, 4, 8, 16, 32, 64, 128]}
TESTf4e82a6525sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree._classes.DecisionTreeClassifier)(1)_pre_dispatch	"2*n_jobs"
TESTf4e82a6525sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree._classes.DecisionTreeClassifier)(1)_random_state	33003
TESTf4e82a6525sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree._classes.DecisionTreeClassifier)(1)_refit	true
TESTf4e82a6525sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree._classes.DecisionTreeClassifier)(1)_return_train_score	false
TESTf4e82a6525sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree._classes.DecisionTreeClassifier)(1)_scoring	null
TESTf4e82a6525sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree._classes.DecisionTreeClassifier)(1)_verbose	0
TESTf4e82a6525sklearn.tree._classes.DecisionTreeClassifier(1)_ccp_alpha	0.0
TESTf4e82a6525sklearn.tree._classes.DecisionTreeClassifier(1)_class_weight	null
TESTf4e82a6525sklearn.tree._classes.DecisionTreeClassifier(1)_criterion	"gini"
TESTf4e82a6525sklearn.tree._classes.DecisionTreeClassifier(1)_max_depth	null
TESTf4e82a6525sklearn.tree._classes.DecisionTreeClassifier(1)_max_features	null
TESTf4e82a6525sklearn.tree._classes.DecisionTreeClassifier(1)_max_leaf_nodes	null
TESTf4e82a6525sklearn.tree._classes.DecisionTreeClassifier(1)_min_impurity_decrease	0.0
TESTf4e82a6525sklearn.tree._classes.DecisionTreeClassifier(1)_min_samples_leaf	1
TESTf4e82a6525sklearn.tree._classes.DecisionTreeClassifier(1)_min_samples_split	2
TESTf4e82a6525sklearn.tree._classes.DecisionTreeClassifier(1)_min_weight_fraction_leaf	0.0
TESTf4e82a6525sklearn.tree._classes.DecisionTreeClassifier(1)_monotonic_cst	null
TESTf4e82a6525sklearn.tree._classes.DecisionTreeClassifier(1)_random_state	62501
TESTf4e82a6525sklearn.tree._classes.DecisionTreeClassifier(1)_splitter	"best"