Run
10459255

Run 10459255

Task 9981 (Supervised Classification) cnae-9 Uploaded 20-05-2020 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.naive_bayes.MultinomialNB),step_1=sklearn.ensemble._forest.Ra ndomForestClassifier)(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_alpha0.9054079294730869
sklearn.ensemble._forest.RandomForestClassifier(2)_class_weightnull
sklearn.ensemble._forest.RandomForestClassifier(2)_criterion"entropy"
sklearn.ensemble._forest.RandomForestClassifier(2)_max_depth631
sklearn.ensemble._forest.RandomForestClassifier(2)_max_features22
sklearn.ensemble._forest.RandomForestClassifier(2)_max_leaf_nodes483
sklearn.ensemble._forest.RandomForestClassifier(2)_max_samples0.6964586454837123
sklearn.ensemble._forest.RandomForestClassifier(2)_min_impurity_decrease0.008353307588011727
sklearn.ensemble._forest.RandomForestClassifier(2)_min_impurity_splitnull
sklearn.ensemble._forest.RandomForestClassifier(2)_min_samples_leaf0.1135719610287071
sklearn.ensemble._forest.RandomForestClassifier(2)_min_samples_split0.2960017889795218
sklearn.ensemble._forest.RandomForestClassifier(2)_min_weight_fraction_leaf0.3883777444462705
sklearn.ensemble._forest.RandomForestClassifier(2)_n_estimators1060
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.naive_bayes.MultinomialNB(6)_alpha0.16807456668496804
sklearn.naive_bayes.MultinomialNB(6)_class_priornull
sklearn.naive_bayes.MultinomialNB(6)_fit_priortrue
sklearn.pipeline.Pipeline(step_0=automl.util.sklearn.StackingEstimator(estimator=sklearn.naive_bayes.MultinomialNB),step_1=sklearn.ensemble._forest.RandomForestClassifier)(1)_memorynull
sklearn.pipeline.Pipeline(step_0=automl.util.sklearn.StackingEstimator(estimator=sklearn.naive_bayes.MultinomialNB),step_1=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"}}]
sklearn.pipeline.Pipeline(step_0=automl.util.sklearn.StackingEstimator(estimator=sklearn.naive_bayes.MultinomialNB),step_1=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

Area under ROC curve
0.8257 ± 0.0073
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.2427 ± 0.007
Cross-validation details (10-fold Crossvalidation)
KB relative information score
0.009 ± 0.001
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.197 ± 0.0001
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.1975
Cross-validation details (10-fold Crossvalidation)
Weighted recall
0.3269 ± 0.0062
Cross-validation details (10-fold Crossvalidation)
Number of instances
1080
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.3269 ± 0.0062
Cross-validation details (10-fold Crossvalidation)
Prior entropy
3.1699
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.9975 ± 0.0003
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.3143
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.3135 ± 0.0001
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.9976 ± 0.0003
Cross-validation details (10-fold Crossvalidation)
Unweighted recall
0.3269 ± 0.0062
Cross-validation details (10-fold Crossvalidation)