Run
17978

Run 17978

Task 801 (Learning Curve) diabetes Uploaded 12-11-2019 by Continuous Integration
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  • openml-python Sklearn_0.22.dev0.
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TEST0da343f807sklearn.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``.
TEST0da343f807sklearn.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
TEST0da343f807sklearn.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"}}]
TEST0da343f807sklearn.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
TEST0da343f807sklearn.impute._base.SimpleImputer(1)_add_indicatorfalse
TEST0da343f807sklearn.impute._base.SimpleImputer(1)_copytrue
TEST0da343f807sklearn.impute._base.SimpleImputer(1)_fill_valuenull
TEST0da343f807sklearn.impute._base.SimpleImputer(1)_missing_valuesNaN
TEST0da343f807sklearn.impute._base.SimpleImputer(1)_strategy"median"
TEST0da343f807sklearn.impute._base.SimpleImputer(1)_verbose0
TEST0da343f807sklearn.feature_selection._variance_threshold.VarianceThreshold(1)_threshold0.0
TEST0da343f807sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree._classes.DecisionTreeClassifier)(1)_cv3
TEST0da343f807sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree._classes.DecisionTreeClassifier)(1)_error_scoreNaN
TEST0da343f807sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree._classes.DecisionTreeClassifier)(1)_iid"deprecated"
TEST0da343f807sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree._classes.DecisionTreeClassifier)(1)_n_iter10
TEST0da343f807sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree._classes.DecisionTreeClassifier)(1)_n_jobsnull
TEST0da343f807sklearn.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]}
TEST0da343f807sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree._classes.DecisionTreeClassifier)(1)_pre_dispatch"2*n_jobs"
TEST0da343f807sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree._classes.DecisionTreeClassifier)(1)_random_state33003
TEST0da343f807sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree._classes.DecisionTreeClassifier)(1)_refittrue
TEST0da343f807sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree._classes.DecisionTreeClassifier)(1)_return_train_scorefalse
TEST0da343f807sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree._classes.DecisionTreeClassifier)(1)_scoringnull
TEST0da343f807sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree._classes.DecisionTreeClassifier)(1)_verbose0
TEST0da343f807sklearn.tree._classes.DecisionTreeClassifier(1)_ccp_alpha0.0
TEST0da343f807sklearn.tree._classes.DecisionTreeClassifier(1)_class_weightnull
TEST0da343f807sklearn.tree._classes.DecisionTreeClassifier(1)_criterion"gini"
TEST0da343f807sklearn.tree._classes.DecisionTreeClassifier(1)_max_depthnull
TEST0da343f807sklearn.tree._classes.DecisionTreeClassifier(1)_max_featuresnull
TEST0da343f807sklearn.tree._classes.DecisionTreeClassifier(1)_max_leaf_nodesnull
TEST0da343f807sklearn.tree._classes.DecisionTreeClassifier(1)_min_impurity_decrease0.0
TEST0da343f807sklearn.tree._classes.DecisionTreeClassifier(1)_min_impurity_splitnull
TEST0da343f807sklearn.tree._classes.DecisionTreeClassifier(1)_min_samples_leaf1
TEST0da343f807sklearn.tree._classes.DecisionTreeClassifier(1)_min_samples_split2
TEST0da343f807sklearn.tree._classes.DecisionTreeClassifier(1)_min_weight_fraction_leaf0.0
TEST0da343f807sklearn.tree._classes.DecisionTreeClassifier(1)_presort"deprecated"
TEST0da343f807sklearn.tree._classes.DecisionTreeClassifier(1)_random_state62501
TEST0da343f807sklearn.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