Run
9361

Run 9361

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

TEST8090602a69sklearn.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 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.
TEST8090602a69sklearn.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"}}]
TEST8090602a69sklearn.preprocessing.imputation.Imputer(1)_axis0
TEST8090602a69sklearn.preprocessing.imputation.Imputer(1)_copytrue
TEST8090602a69sklearn.preprocessing.imputation.Imputer(1)_missing_values"NaN"
TEST8090602a69sklearn.preprocessing.imputation.Imputer(1)_strategy"median"
TEST8090602a69sklearn.preprocessing.imputation.Imputer(1)_verbose0
TEST8090602a69sklearn.feature_selection.variance_threshold.VarianceThreshold(1)_threshold0.0
TEST8090602a69sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree.tree.DecisionTreeClassifier)(1)_cv3
TEST8090602a69sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree.tree.DecisionTreeClassifier)(1)_error_score"raise"
TEST8090602a69sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree.tree.DecisionTreeClassifier)(1)_fit_params{}
TEST8090602a69sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree.tree.DecisionTreeClassifier)(1)_iidtrue
TEST8090602a69sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree.tree.DecisionTreeClassifier)(1)_n_iter10
TEST8090602a69sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree.tree.DecisionTreeClassifier)(1)_n_jobs1
TEST8090602a69sklearn.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]}
TEST8090602a69sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree.tree.DecisionTreeClassifier)(1)_pre_dispatch"2*n_jobs"
TEST8090602a69sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree.tree.DecisionTreeClassifier)(1)_random_state33003
TEST8090602a69sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree.tree.DecisionTreeClassifier)(1)_refittrue
TEST8090602a69sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree.tree.DecisionTreeClassifier)(1)_return_train_scoretrue
TEST8090602a69sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree.tree.DecisionTreeClassifier)(1)_scoringnull
TEST8090602a69sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree.tree.DecisionTreeClassifier)(1)_verbose0
TEST8090602a69sklearn.tree.tree.DecisionTreeClassifier(1)_class_weightnull
TEST8090602a69sklearn.tree.tree.DecisionTreeClassifier(1)_criterion"gini"
TEST8090602a69sklearn.tree.tree.DecisionTreeClassifier(1)_max_depthnull
TEST8090602a69sklearn.tree.tree.DecisionTreeClassifier(1)_max_featuresnull
TEST8090602a69sklearn.tree.tree.DecisionTreeClassifier(1)_max_leaf_nodesnull
TEST8090602a69sklearn.tree.tree.DecisionTreeClassifier(1)_min_impurity_split1e-07
TEST8090602a69sklearn.tree.tree.DecisionTreeClassifier(1)_min_samples_leaf1
TEST8090602a69sklearn.tree.tree.DecisionTreeClassifier(1)_min_samples_split2
TEST8090602a69sklearn.tree.tree.DecisionTreeClassifier(1)_min_weight_fraction_leaf0.0
TEST8090602a69sklearn.tree.tree.DecisionTreeClassifier(1)_presortfalse
TEST8090602a69sklearn.tree.tree.DecisionTreeClassifier(1)_random_state62501
TEST8090602a69sklearn.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