OpenML
3395

Run 3395

Task 801 (Learning Curve) diabetes Uploaded 12-01-2024 by Continuous Integration
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TESTf6cdb9b6acsklearn.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``.
TESTf6cdb9b6acsklearn.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
TESTf6cdb9b6acsklearn.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"}}]
TESTf6cdb9b6acsklearn.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
TESTf6cdb9b6acsklearn.impute._base.SimpleImputer(1)_add_indicatorfalse
TESTf6cdb9b6acsklearn.impute._base.SimpleImputer(1)_copytrue
TESTf6cdb9b6acsklearn.impute._base.SimpleImputer(1)_fill_valuenull
TESTf6cdb9b6acsklearn.impute._base.SimpleImputer(1)_missing_valuesNaN
TESTf6cdb9b6acsklearn.impute._base.SimpleImputer(1)_strategy"median"
TESTf6cdb9b6acsklearn.impute._base.SimpleImputer(1)_verbose0
TESTf6cdb9b6acsklearn.feature_selection._variance_threshold.VarianceThreshold(1)_threshold0.0
TESTf6cdb9b6acsklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree._classes.DecisionTreeClassifier)(1)_cv3
TESTf6cdb9b6acsklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree._classes.DecisionTreeClassifier)(1)_error_scoreNaN
TESTf6cdb9b6acsklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree._classes.DecisionTreeClassifier)(1)_n_iter10
TESTf6cdb9b6acsklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree._classes.DecisionTreeClassifier)(1)_n_jobsnull
TESTf6cdb9b6acsklearn.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]}
TESTf6cdb9b6acsklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree._classes.DecisionTreeClassifier)(1)_pre_dispatch"2*n_jobs"
TESTf6cdb9b6acsklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree._classes.DecisionTreeClassifier)(1)_random_state33003
TESTf6cdb9b6acsklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree._classes.DecisionTreeClassifier)(1)_refittrue
TESTf6cdb9b6acsklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree._classes.DecisionTreeClassifier)(1)_return_train_scorefalse
TESTf6cdb9b6acsklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree._classes.DecisionTreeClassifier)(1)_scoringnull
TESTf6cdb9b6acsklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree._classes.DecisionTreeClassifier)(1)_verbose0
TESTf6cdb9b6acsklearn.tree._classes.DecisionTreeClassifier(1)_ccp_alpha0.0
TESTf6cdb9b6acsklearn.tree._classes.DecisionTreeClassifier(1)_class_weightnull
TESTf6cdb9b6acsklearn.tree._classes.DecisionTreeClassifier(1)_criterion"gini"
TESTf6cdb9b6acsklearn.tree._classes.DecisionTreeClassifier(1)_max_depthnull
TESTf6cdb9b6acsklearn.tree._classes.DecisionTreeClassifier(1)_max_featuresnull
TESTf6cdb9b6acsklearn.tree._classes.DecisionTreeClassifier(1)_max_leaf_nodesnull
TESTf6cdb9b6acsklearn.tree._classes.DecisionTreeClassifier(1)_min_impurity_decrease0.0
TESTf6cdb9b6acsklearn.tree._classes.DecisionTreeClassifier(1)_min_impurity_splitnull
TESTf6cdb9b6acsklearn.tree._classes.DecisionTreeClassifier(1)_min_samples_leaf1
TESTf6cdb9b6acsklearn.tree._classes.DecisionTreeClassifier(1)_min_samples_split2
TESTf6cdb9b6acsklearn.tree._classes.DecisionTreeClassifier(1)_min_weight_fraction_leaf0.0
TESTf6cdb9b6acsklearn.tree._classes.DecisionTreeClassifier(1)_random_state62501
TESTf6cdb9b6acsklearn.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

Area under ROC curve
0.8109 ± 0.0386
Per class
F measure
0.7383 ± 0.0432
Per class
Kappa
0.4175 ± 0.0983
KB relative information score
0.3291 ± 0.0605
Mean absolute error
0.305 ± 0.026
Mean prior absolute error
0.4545 ± 0.0015
Weighted recall
0.7422 ± 0.0393
Number of instances
768
Per class
Precision
0.7368 ± 0.0413
Per class
Predictive accuracy
0.7422 ± 0.0393
Prior entropy
0.9331 ± 0.0046
Relative absolute error
0.6711 ± 0.0566
Root mean prior squared error
0.4766 ± 0.0016
Root mean squared error
0.4093 ± 0.0263
Root relative squared error
0.8588 ± 0.0541
Unweighted recall
0.7033 ± 0.0515