Run
10457992

Run 10457992

Task 9981 (Supervised Classification) cnae-9 Uploaded 19-05-2020 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=automl.util.sklearn.StackingEstimator(esti mator=sklearn.ensemble._forest.RandomForestClassifier),step_1=sklearn.svm._ classes.SVC)(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_alpha0.3375163104633011
sklearn.ensemble._forest.RandomForestClassifier(2)_class_weightnull
sklearn.ensemble._forest.RandomForestClassifier(2)_criterion"gini"
sklearn.ensemble._forest.RandomForestClassifier(2)_max_depth2
sklearn.ensemble._forest.RandomForestClassifier(2)_max_features2
sklearn.ensemble._forest.RandomForestClassifier(2)_max_leaf_nodes611
sklearn.ensemble._forest.RandomForestClassifier(2)_max_samples0.4452731749453411
sklearn.ensemble._forest.RandomForestClassifier(2)_min_impurity_decrease0.40232511164531315
sklearn.ensemble._forest.RandomForestClassifier(2)_min_impurity_splitnull
sklearn.ensemble._forest.RandomForestClassifier(2)_min_samples_leaf0.29545591375110225
sklearn.ensemble._forest.RandomForestClassifier(2)_min_samples_split0.29964793706290954
sklearn.ensemble._forest.RandomForestClassifier(2)_min_weight_fraction_leaf0.2605194621489635
sklearn.ensemble._forest.RandomForestClassifier(2)_n_estimators1707
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.svm._classes.SVC(4)_C1.247825878607968
sklearn.svm._classes.SVC(4)_break_tiestrue
sklearn.svm._classes.SVC(4)_cache_size200
sklearn.svm._classes.SVC(4)_class_weightnull
sklearn.svm._classes.SVC(4)_coef0-22.616429978138296
sklearn.svm._classes.SVC(4)_decision_function_shape"ovr"
sklearn.svm._classes.SVC(4)_degree5
sklearn.svm._classes.SVC(4)_gamma0.00012668165195389572
sklearn.svm._classes.SVC(4)_kernel"poly"
sklearn.svm._classes.SVC(4)_max_iter-1
sklearn.svm._classes.SVC(4)_probabilityfalse
sklearn.svm._classes.SVC(4)_random_state42
sklearn.svm._classes.SVC(4)_shrinkingtrue
sklearn.svm._classes.SVC(4)_tol0.020831590041147475
sklearn.svm._classes.SVC(4)_verbosefalse
sklearn.pipeline.Pipeline(step_0=automl.util.sklearn.StackingEstimator(estimator=sklearn.ensemble._forest.RandomForestClassifier),step_1=sklearn.svm._classes.SVC)(1)_memorynull
sklearn.pipeline.Pipeline(step_0=automl.util.sklearn.StackingEstimator(estimator=sklearn.ensemble._forest.RandomForestClassifier),step_1=sklearn.svm._classes.SVC)(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"}}]
sklearn.pipeline.Pipeline(step_0=automl.util.sklearn.StackingEstimator(estimator=sklearn.ensemble._forest.RandomForestClassifier),step_1=sklearn.svm._classes.SVC)(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.

18 Evaluation measures

Area under ROC curve
0.9505 ± 0.0174
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.9122 ± 0.0311
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.901 ± 0.0348
Cross-validation details (10-fold Crossvalidation)
KB relative information score
0.9073 ± 0.0326
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.0195 ± 0.0069
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.1975
Cross-validation details (10-fold Crossvalidation)
Weighted recall
0.912 ± 0.0309
Cross-validation details (10-fold Crossvalidation)
Number of instances
1080
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.9156 ± 0.0289
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.912 ± 0.0309
Cross-validation details (10-fold Crossvalidation)
Prior entropy
3.1699
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.099 ± 0.0348
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.3143
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.1398 ± 0.0237
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.4449 ± 0.0753
Cross-validation details (10-fold Crossvalidation)
Unweighted recall
0.912 ± 0.0309
Cross-validation details (10-fold Crossvalidation)