Run
10455273

Run 10455273

Task 9914 (Supervised Classification) wilt 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)_bootstraptrue
sklearn.ensemble._forest.RandomForestClassifier(2)_ccp_alpha0.15729795389267487
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_features1
sklearn.ensemble._forest.RandomForestClassifier(2)_max_leaf_nodes694
sklearn.ensemble._forest.RandomForestClassifier(2)_max_samples0.05075830788639543
sklearn.ensemble._forest.RandomForestClassifier(2)_min_impurity_decrease0.012716799793273031
sklearn.ensemble._forest.RandomForestClassifier(2)_min_impurity_splitnull
sklearn.ensemble._forest.RandomForestClassifier(2)_min_samples_leaf0.21734634351249574
sklearn.ensemble._forest.RandomForestClassifier(2)_min_samples_split0.1725648393867974
sklearn.ensemble._forest.RandomForestClassifier(2)_min_weight_fraction_leaf0.00027832687946660606
sklearn.ensemble._forest.RandomForestClassifier(2)_n_estimators1051
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)_C1366.12991274513
sklearn.svm._classes.SVC(4)_break_tiesfalse
sklearn.svm._classes.SVC(4)_cache_size200
sklearn.svm._classes.SVC(4)_class_weightnull
sklearn.svm._classes.SVC(4)_coef00.0
sklearn.svm._classes.SVC(4)_decision_function_shape"ovr"
sklearn.svm._classes.SVC(4)_degree3
sklearn.svm._classes.SVC(4)_gamma1.3268279358126264e-07
sklearn.svm._classes.SVC(4)_kernel"rbf"
sklearn.svm._classes.SVC(4)_max_iter-1
sklearn.svm._classes.SVC(4)_probabilitytrue
sklearn.svm._classes.SVC(4)_random_state42
sklearn.svm._classes.SVC(4)_shrinkingtrue
sklearn.svm._classes.SVC(4)_tol2.2240847493962436e-05
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
0.9889 ± 0.0053
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.9764 ± 0.0043
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.7628 ± 0.0503
Cross-validation details (10-fold Crossvalidation)
KB relative information score
0.5374 ± 0.0505
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.0327 ± 0.0021
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.1022 ± 0.0006
Cross-validation details (10-fold Crossvalidation)
Weighted recall
0.9771 ± 0.0037
Cross-validation details (10-fold Crossvalidation)
Number of instances
4839
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.9761 ± 0.0046
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.9771 ± 0.0037
Cross-validation details (10-fold Crossvalidation)
Prior entropy
0.3029 ± 0.0027
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.3197 ± 0.0203
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.2259 ± 0.0013
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.1275 ± 0.0083
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.5644 ± 0.0361
Cross-validation details (10-fold Crossvalidation)
Unweighted recall
0.8614 ± 0.0468
Cross-validation details (10-fold Crossvalidation)