OpenML
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Run 530

Task 801 (Learning Curve) diabetes Uploaded 11-01-2024 by Continuous Integration
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Flow

TEST6499fd3fd1sklearn.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``.
TEST6499fd3fd1sklearn.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
TEST6499fd3fd1sklearn.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"}}]
TEST6499fd3fd1sklearn.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
TEST6499fd3fd1sklearn.impute._base.SimpleImputer(1)_add_indicatorfalse
TEST6499fd3fd1sklearn.impute._base.SimpleImputer(1)_copytrue
TEST6499fd3fd1sklearn.impute._base.SimpleImputer(1)_fill_valuenull
TEST6499fd3fd1sklearn.impute._base.SimpleImputer(1)_missing_valuesNaN
TEST6499fd3fd1sklearn.impute._base.SimpleImputer(1)_strategy"median"
TEST6499fd3fd1sklearn.impute._base.SimpleImputer(1)_verbose0
TEST6499fd3fd1sklearn.feature_selection._variance_threshold.VarianceThreshold(1)_threshold0.0
TEST6499fd3fd1sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree._classes.DecisionTreeClassifier)(1)_cv3
TEST6499fd3fd1sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree._classes.DecisionTreeClassifier)(1)_error_scoreNaN
TEST6499fd3fd1sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree._classes.DecisionTreeClassifier)(1)_n_iter10
TEST6499fd3fd1sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree._classes.DecisionTreeClassifier)(1)_n_jobsnull
TEST6499fd3fd1sklearn.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]}
TEST6499fd3fd1sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree._classes.DecisionTreeClassifier)(1)_pre_dispatch"2*n_jobs"
TEST6499fd3fd1sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree._classes.DecisionTreeClassifier)(1)_random_state33003
TEST6499fd3fd1sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree._classes.DecisionTreeClassifier)(1)_refittrue
TEST6499fd3fd1sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree._classes.DecisionTreeClassifier)(1)_return_train_scorefalse
TEST6499fd3fd1sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree._classes.DecisionTreeClassifier)(1)_scoringnull
TEST6499fd3fd1sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree._classes.DecisionTreeClassifier)(1)_verbose0
TEST6499fd3fd1sklearn.tree._classes.DecisionTreeClassifier(1)_ccp_alpha0.0
TEST6499fd3fd1sklearn.tree._classes.DecisionTreeClassifier(1)_class_weightnull
TEST6499fd3fd1sklearn.tree._classes.DecisionTreeClassifier(1)_criterion"gini"
TEST6499fd3fd1sklearn.tree._classes.DecisionTreeClassifier(1)_max_depthnull
TEST6499fd3fd1sklearn.tree._classes.DecisionTreeClassifier(1)_max_featuresnull
TEST6499fd3fd1sklearn.tree._classes.DecisionTreeClassifier(1)_max_leaf_nodesnull
TEST6499fd3fd1sklearn.tree._classes.DecisionTreeClassifier(1)_min_impurity_decrease0.0
TEST6499fd3fd1sklearn.tree._classes.DecisionTreeClassifier(1)_min_impurity_splitnull
TEST6499fd3fd1sklearn.tree._classes.DecisionTreeClassifier(1)_min_samples_leaf1
TEST6499fd3fd1sklearn.tree._classes.DecisionTreeClassifier(1)_min_samples_split2
TEST6499fd3fd1sklearn.tree._classes.DecisionTreeClassifier(1)_min_weight_fraction_leaf0.0
TEST6499fd3fd1sklearn.tree._classes.DecisionTreeClassifier(1)_random_state62501
TEST6499fd3fd1sklearn.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