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2351

Run 2351

Task 801 (Learning Curve) diabetes Uploaded 12-01-2024 by Continuous Integration
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TESTb246c236afsklearn.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`. For an example use case of `Pipeline` combined with :class:`~sklearn.model_selection.GridSearchCV`, refer to :ref:`sphx_glr_auto_examples_compose_plot_compare_reduction.py`. The example :ref:`sphx_glr_auto_exampl...
TESTb246c236afsklearn.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
TESTb246c236afsklearn.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"}}]
TESTb246c236afsklearn.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
TESTb246c236afsklearn.impute._base.SimpleImputer(1)_add_indicatorfalse
TESTb246c236afsklearn.impute._base.SimpleImputer(1)_copytrue
TESTb246c236afsklearn.impute._base.SimpleImputer(1)_fill_valuenull
TESTb246c236afsklearn.impute._base.SimpleImputer(1)_keep_empty_featuresfalse
TESTb246c236afsklearn.impute._base.SimpleImputer(1)_missing_valuesNaN
TESTb246c236afsklearn.impute._base.SimpleImputer(1)_strategy"median"
TESTb246c236afsklearn.feature_selection._variance_threshold.VarianceThreshold(1)_threshold0.0
TESTb246c236afsklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree._classes.DecisionTreeClassifier)(1)_cv3
TESTb246c236afsklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree._classes.DecisionTreeClassifier)(1)_error_scoreNaN
TESTb246c236afsklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree._classes.DecisionTreeClassifier)(1)_n_iter10
TESTb246c236afsklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree._classes.DecisionTreeClassifier)(1)_n_jobsnull
TESTb246c236afsklearn.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]}
TESTb246c236afsklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree._classes.DecisionTreeClassifier)(1)_pre_dispatch"2*n_jobs"
TESTb246c236afsklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree._classes.DecisionTreeClassifier)(1)_random_state33003
TESTb246c236afsklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree._classes.DecisionTreeClassifier)(1)_refittrue
TESTb246c236afsklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree._classes.DecisionTreeClassifier)(1)_return_train_scorefalse
TESTb246c236afsklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree._classes.DecisionTreeClassifier)(1)_scoringnull
TESTb246c236afsklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree._classes.DecisionTreeClassifier)(1)_verbose0
TESTb246c236afsklearn.tree._classes.DecisionTreeClassifier(1)_ccp_alpha0.0
TESTb246c236afsklearn.tree._classes.DecisionTreeClassifier(1)_class_weightnull
TESTb246c236afsklearn.tree._classes.DecisionTreeClassifier(1)_criterion"gini"
TESTb246c236afsklearn.tree._classes.DecisionTreeClassifier(1)_max_depthnull
TESTb246c236afsklearn.tree._classes.DecisionTreeClassifier(1)_max_featuresnull
TESTb246c236afsklearn.tree._classes.DecisionTreeClassifier(1)_max_leaf_nodesnull
TESTb246c236afsklearn.tree._classes.DecisionTreeClassifier(1)_min_impurity_decrease0.0
TESTb246c236afsklearn.tree._classes.DecisionTreeClassifier(1)_min_samples_leaf1
TESTb246c236afsklearn.tree._classes.DecisionTreeClassifier(1)_min_samples_split2
TESTb246c236afsklearn.tree._classes.DecisionTreeClassifier(1)_min_weight_fraction_leaf0.0
TESTb246c236afsklearn.tree._classes.DecisionTreeClassifier(1)_random_state62501
TESTb246c236afsklearn.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