Run
9396

Run 9396

Task 801 (Learning Curve) diabetes Uploaded 25-11-2022 by Continuous Integration
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TESTff7f3587d4sklearn.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)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``.
TESTff7f3587d4sklearn.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)_memorynull
TESTff7f3587d4sklearn.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"}}]
TESTff7f3587d4sklearn.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)_verbosefalse
TESTff7f3587d4sklearn.impute._base.SimpleImputer(1)_add_indicatorfalse
TESTff7f3587d4sklearn.impute._base.SimpleImputer(1)_copytrue
TESTff7f3587d4sklearn.impute._base.SimpleImputer(1)_fill_valuenull
TESTff7f3587d4sklearn.impute._base.SimpleImputer(1)_missing_valuesNaN
TESTff7f3587d4sklearn.impute._base.SimpleImputer(1)_strategy"median"
TESTff7f3587d4sklearn.impute._base.SimpleImputer(1)_verbose0
TESTff7f3587d4sklearn.feature_selection._variance_threshold.VarianceThreshold(1)_threshold0.0
TESTff7f3587d4sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree._classes.DecisionTreeClassifier)(1)_cv3
TESTff7f3587d4sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree._classes.DecisionTreeClassifier)(1)_error_scoreNaN
TESTff7f3587d4sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree._classes.DecisionTreeClassifier)(1)_n_iter10
TESTff7f3587d4sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree._classes.DecisionTreeClassifier)(1)_n_jobsnull
TESTff7f3587d4sklearn.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]}
TESTff7f3587d4sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree._classes.DecisionTreeClassifier)(1)_pre_dispatch"2*n_jobs"
TESTff7f3587d4sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree._classes.DecisionTreeClassifier)(1)_random_state33003
TESTff7f3587d4sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree._classes.DecisionTreeClassifier)(1)_refittrue
TESTff7f3587d4sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree._classes.DecisionTreeClassifier)(1)_return_train_scorefalse
TESTff7f3587d4sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree._classes.DecisionTreeClassifier)(1)_scoringnull
TESTff7f3587d4sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree._classes.DecisionTreeClassifier)(1)_verbose0
TESTff7f3587d4sklearn.tree._classes.DecisionTreeClassifier(1)_ccp_alpha0.0
TESTff7f3587d4sklearn.tree._classes.DecisionTreeClassifier(1)_class_weightnull
TESTff7f3587d4sklearn.tree._classes.DecisionTreeClassifier(1)_criterion"gini"
TESTff7f3587d4sklearn.tree._classes.DecisionTreeClassifier(1)_max_depthnull
TESTff7f3587d4sklearn.tree._classes.DecisionTreeClassifier(1)_max_featuresnull
TESTff7f3587d4sklearn.tree._classes.DecisionTreeClassifier(1)_max_leaf_nodesnull
TESTff7f3587d4sklearn.tree._classes.DecisionTreeClassifier(1)_min_impurity_decrease0.0
TESTff7f3587d4sklearn.tree._classes.DecisionTreeClassifier(1)_min_impurity_splitnull
TESTff7f3587d4sklearn.tree._classes.DecisionTreeClassifier(1)_min_samples_leaf1
TESTff7f3587d4sklearn.tree._classes.DecisionTreeClassifier(1)_min_samples_split2
TESTff7f3587d4sklearn.tree._classes.DecisionTreeClassifier(1)_min_weight_fraction_leaf0.0
TESTff7f3587d4sklearn.tree._classes.DecisionTreeClassifier(1)_random_state62501
TESTff7f3587d4sklearn.tree._classes.DecisionTreeClassifier(1)_splitter"best"

Result files

xml
Description

XML file describing the run, including user-defined evaluation measures.

arff
Predictions

ARFF file with instance-level predictions generated by the model.

18 Evaluation measures

0.8109 ± 0.0386
Per class
0.7383 ± 0.0432
Per class
0.4175 ± 0.0983
0.3291 ± 0.0605
0.305 ± 0.026
0.4545 ± 0.0015
0.7422 ± 0.0393
768
Per class
0.7368 ± 0.0413
Per class
0.7422 ± 0.0393
0.9331 ± 0.0046
0.6711 ± 0.0566
0.4766 ± 0.0016
0.4093 ± 0.0263
0.8588 ± 0.0541
0.7033 ± 0.0515