Run
153

Run 153

Task 801 (Learning Curve) diabetes Uploaded 29-10-2019 by Continuous Integration
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TEST3564304c30sklearn.pipeline.Pipeline(Imputer=sklearn.impute._base.Simple Imputer,VarianceThreshold=sklearn.feature_selection.variance_threshold.Vari anceThreshold,Estimator=sklearn.model_selection._search.RandomizedSearchCV( 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 it to 'passthrough' or ``None``.
TEST3564304c30sklearn.pipeline.Pipeline(Imputer=sklearn.impute._base.SimpleImputer,VarianceThreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold,Estimator=sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree.tree.DecisionTreeClassifier))(1)_memorynull
TEST3564304c30sklearn.pipeline.Pipeline(Imputer=sklearn.impute._base.SimpleImputer,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"}}]
TEST3564304c30sklearn.pipeline.Pipeline(Imputer=sklearn.impute._base.SimpleImputer,VarianceThreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold,Estimator=sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree.tree.DecisionTreeClassifier))(1)_verbosefalse
TEST3564304c30sklearn.impute._base.SimpleImputer(1)_add_indicatorfalse
TEST3564304c30sklearn.impute._base.SimpleImputer(1)_copytrue
TEST3564304c30sklearn.impute._base.SimpleImputer(1)_fill_valuenull
TEST3564304c30sklearn.impute._base.SimpleImputer(1)_missing_valuesNaN
TEST3564304c30sklearn.impute._base.SimpleImputer(1)_strategy"median"
TEST3564304c30sklearn.impute._base.SimpleImputer(1)_verbose0
TEST3564304c30sklearn.feature_selection.variance_threshold.VarianceThreshold(1)_threshold0.0
TEST3564304c30sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree.tree.DecisionTreeClassifier)(1)_cv3
TEST3564304c30sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree.tree.DecisionTreeClassifier)(1)_error_score"raise-deprecating"
TEST3564304c30sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree.tree.DecisionTreeClassifier)(1)_iid"warn"
TEST3564304c30sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree.tree.DecisionTreeClassifier)(1)_n_iter10
TEST3564304c30sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree.tree.DecisionTreeClassifier)(1)_n_jobsnull
TEST3564304c30sklearn.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]}
TEST3564304c30sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree.tree.DecisionTreeClassifier)(1)_pre_dispatch"2*n_jobs"
TEST3564304c30sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree.tree.DecisionTreeClassifier)(1)_random_state33003
TEST3564304c30sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree.tree.DecisionTreeClassifier)(1)_refittrue
TEST3564304c30sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree.tree.DecisionTreeClassifier)(1)_return_train_scorefalse
TEST3564304c30sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree.tree.DecisionTreeClassifier)(1)_scoringnull
TEST3564304c30sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree.tree.DecisionTreeClassifier)(1)_verbose0
TEST3564304c30sklearn.tree.tree.DecisionTreeClassifier(1)_class_weightnull
TEST3564304c30sklearn.tree.tree.DecisionTreeClassifier(1)_criterion"gini"
TEST3564304c30sklearn.tree.tree.DecisionTreeClassifier(1)_max_depthnull
TEST3564304c30sklearn.tree.tree.DecisionTreeClassifier(1)_max_featuresnull
TEST3564304c30sklearn.tree.tree.DecisionTreeClassifier(1)_max_leaf_nodesnull
TEST3564304c30sklearn.tree.tree.DecisionTreeClassifier(1)_min_impurity_decrease0.0
TEST3564304c30sklearn.tree.tree.DecisionTreeClassifier(1)_min_impurity_splitnull
TEST3564304c30sklearn.tree.tree.DecisionTreeClassifier(1)_min_samples_leaf1
TEST3564304c30sklearn.tree.tree.DecisionTreeClassifier(1)_min_samples_split2
TEST3564304c30sklearn.tree.tree.DecisionTreeClassifier(1)_min_weight_fraction_leaf0.0
TEST3564304c30sklearn.tree.tree.DecisionTreeClassifier(1)_presortfalse
TEST3564304c30sklearn.tree.tree.DecisionTreeClassifier(1)_random_state62501
TEST3564304c30sklearn.tree.tree.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