Run
9332

Run 9332

Task 801 (Learning Curve) diabetes Uploaded 25-11-2022 by Continuous Integration
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Flow

TEST0957561bd4sklearn.pipeline.Pipeline(Imputer=sklearn.preprocessing.imput ation.Imputer,VarianceThreshold=sklearn.feature_selection.variance_threshol d.VarianceThreshold,Estimator=sklearn.model_selection._search.RandomizedSea rchCV(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 to None.
TEST0957561bd4sklearn.pipeline.Pipeline(Imputer=sklearn.preprocessing.imputation.Imputer,VarianceThreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold,Estimator=sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree.tree.DecisionTreeClassifier))(1)_memorynull
TEST0957561bd4sklearn.pipeline.Pipeline(Imputer=sklearn.preprocessing.imputation.Imputer,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"}}]
TEST0957561bd4sklearn.preprocessing.imputation.Imputer(1)_axis0
TEST0957561bd4sklearn.preprocessing.imputation.Imputer(1)_copytrue
TEST0957561bd4sklearn.preprocessing.imputation.Imputer(1)_missing_values"NaN"
TEST0957561bd4sklearn.preprocessing.imputation.Imputer(1)_strategy"median"
TEST0957561bd4sklearn.preprocessing.imputation.Imputer(1)_verbose0
TEST0957561bd4sklearn.feature_selection.variance_threshold.VarianceThreshold(1)_threshold0.0
TEST0957561bd4sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree.tree.DecisionTreeClassifier)(1)_cv3
TEST0957561bd4sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree.tree.DecisionTreeClassifier)(1)_error_score"raise"
TEST0957561bd4sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree.tree.DecisionTreeClassifier)(1)_fit_paramsnull
TEST0957561bd4sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree.tree.DecisionTreeClassifier)(1)_iidtrue
TEST0957561bd4sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree.tree.DecisionTreeClassifier)(1)_n_iter10
TEST0957561bd4sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree.tree.DecisionTreeClassifier)(1)_n_jobs1
TEST0957561bd4sklearn.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]}
TEST0957561bd4sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree.tree.DecisionTreeClassifier)(1)_pre_dispatch"2*n_jobs"
TEST0957561bd4sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree.tree.DecisionTreeClassifier)(1)_random_state33003
TEST0957561bd4sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree.tree.DecisionTreeClassifier)(1)_refittrue
TEST0957561bd4sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree.tree.DecisionTreeClassifier)(1)_return_train_score"warn"
TEST0957561bd4sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree.tree.DecisionTreeClassifier)(1)_scoringnull
TEST0957561bd4sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree.tree.DecisionTreeClassifier)(1)_verbose0
TEST0957561bd4sklearn.tree.tree.DecisionTreeClassifier(1)_class_weightnull
TEST0957561bd4sklearn.tree.tree.DecisionTreeClassifier(1)_criterion"gini"
TEST0957561bd4sklearn.tree.tree.DecisionTreeClassifier(1)_max_depthnull
TEST0957561bd4sklearn.tree.tree.DecisionTreeClassifier(1)_max_featuresnull
TEST0957561bd4sklearn.tree.tree.DecisionTreeClassifier(1)_max_leaf_nodesnull
TEST0957561bd4sklearn.tree.tree.DecisionTreeClassifier(1)_min_impurity_decrease0.0
TEST0957561bd4sklearn.tree.tree.DecisionTreeClassifier(1)_min_impurity_splitnull
TEST0957561bd4sklearn.tree.tree.DecisionTreeClassifier(1)_min_samples_leaf1
TEST0957561bd4sklearn.tree.tree.DecisionTreeClassifier(1)_min_samples_split2
TEST0957561bd4sklearn.tree.tree.DecisionTreeClassifier(1)_min_weight_fraction_leaf0.0
TEST0957561bd4sklearn.tree.tree.DecisionTreeClassifier(1)_presortfalse
TEST0957561bd4sklearn.tree.tree.DecisionTreeClassifier(1)_random_state62501
TEST0957561bd4sklearn.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