Run
10463532

Run 10463532

Task 3021 (Supervised Classification) sick Uploaded 21-05-2020 by Marc Zöller
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  • automl_meta_features openml-python Sklearn_0.22.1.
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Flow

sklearn.pipeline.Pipeline(step_0=sklearn.impute._base.MissingIndicator,step _1=sklearn.preprocessing._discretization.KBinsDiscretizer,step_2=sklearn.en semble._forest.RandomForestClassifier)(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``.
sklearn.ensemble._forest.RandomForestClassifier(2)_bootstrapfalse
sklearn.ensemble._forest.RandomForestClassifier(2)_ccp_alpha0.08305394219601137
sklearn.ensemble._forest.RandomForestClassifier(2)_class_weightnull
sklearn.ensemble._forest.RandomForestClassifier(2)_criterion"gini"
sklearn.ensemble._forest.RandomForestClassifier(2)_max_depth12
sklearn.ensemble._forest.RandomForestClassifier(2)_max_features3
sklearn.ensemble._forest.RandomForestClassifier(2)_max_leaf_nodes1909
sklearn.ensemble._forest.RandomForestClassifier(2)_max_samples0.106885904369344
sklearn.ensemble._forest.RandomForestClassifier(2)_min_impurity_decrease0.33785270856124117
sklearn.ensemble._forest.RandomForestClassifier(2)_min_impurity_splitnull
sklearn.ensemble._forest.RandomForestClassifier(2)_min_samples_leaf0.07112566841197981
sklearn.ensemble._forest.RandomForestClassifier(2)_min_samples_split0.3279559130340853
sklearn.ensemble._forest.RandomForestClassifier(2)_min_weight_fraction_leaf0.0770430342264441
sklearn.ensemble._forest.RandomForestClassifier(2)_n_estimators4206
sklearn.ensemble._forest.RandomForestClassifier(2)_n_jobs1
sklearn.ensemble._forest.RandomForestClassifier(2)_oob_scorefalse
sklearn.ensemble._forest.RandomForestClassifier(2)_random_state42
sklearn.ensemble._forest.RandomForestClassifier(2)_verbose0
sklearn.ensemble._forest.RandomForestClassifier(2)_warm_startfalse
sklearn.preprocessing._discretization.KBinsDiscretizer(1)_encode"onehot-dense"
sklearn.preprocessing._discretization.KBinsDiscretizer(1)_n_bins18
sklearn.preprocessing._discretization.KBinsDiscretizer(1)_strategy"kmeans"
sklearn.impute._base.MissingIndicator(1)_error_on_newtrue
sklearn.impute._base.MissingIndicator(1)_features"all"
sklearn.impute._base.MissingIndicator(1)_missing_valuesNaN
sklearn.impute._base.MissingIndicator(1)_sparse"auto"
sklearn.pipeline.Pipeline(step_0=sklearn.impute._base.MissingIndicator,step_1=sklearn.preprocessing._discretization.KBinsDiscretizer,step_2=sklearn.ensemble._forest.RandomForestClassifier)(1)_memorynull
sklearn.pipeline.Pipeline(step_0=sklearn.impute._base.MissingIndicator,step_1=sklearn.preprocessing._discretization.KBinsDiscretizer,step_2=sklearn.ensemble._forest.RandomForestClassifier)(1)_steps[{"oml-python:serialized_object": "component_reference", "value": {"key": "step_0", "step_name": "step_0"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "step_1", "step_name": "step_1"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "step_2", "step_name": "step_2"}}]
sklearn.pipeline.Pipeline(step_0=sklearn.impute._base.MissingIndicator,step_1=sklearn.preprocessing._discretization.KBinsDiscretizer,step_2=sklearn.ensemble._forest.RandomForestClassifier)(1)_verbosefalse

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.

16 Evaluation measures

0.4979
Per class
Cross-validation details (10-fold Crossvalidation)
0.0009 ± 0.0003
Cross-validation details (10-fold Crossvalidation)
0.115 ± 0.0006
Cross-validation details (10-fold Crossvalidation)
0.1152 ± 0.0007
Cross-validation details (10-fold Crossvalidation)
0.9388 ± 0.0008
Cross-validation details (10-fold Crossvalidation)
3772
Per class
Cross-validation details (10-fold Crossvalidation)
0.9388 ± 0.0008
Cross-validation details (10-fold Crossvalidation)
0.3324 ± 0.0031
Cross-validation details (10-fold Crossvalidation)
0.9982 ± 0.0006
Cross-validation details (10-fold Crossvalidation)
0.2398 ± 0.0014
Cross-validation details (10-fold Crossvalidation)
0.2398 ± 0.0014
Cross-validation details (10-fold Crossvalidation)
1 ± 0
Cross-validation details (10-fold Crossvalidation)
0.5
Cross-validation details (10-fold Crossvalidation)