Run
4501

Run 4501

Task 115 (Supervised Classification) diabetes Uploaded 18-10-2024 by Jan van Rijn
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Flow

weka.classifiers.meta.MultiSearch(weka.classifiers.meta.multisearch.RandomS earch,weka.classifiers.meta.FilteredClassifier(weka.filters.MultiFilter(wek a.filters.unsupervised.attribute.ReplaceMissingValues,weka.filters.unsuperv ised.attribute.RemoveUseless,weka.filters.unsupervised.attribute.Normalize) ,weka.classifiers.meta.LogitBoost(weka.classifiers.trees.REPTree)))(1)Weka implementation.
weka.classifiers.meta.multisearch.RandomSearch(1)_sample-size["100.0"]
weka.classifiers.meta.multisearch.RandomSearch(1)_num-folds["3"]
weka.classifiers.meta.multisearch.RandomSearch(1)_test-set["."]
weka.classifiers.meta.multisearch.RandomSearch(1)_num-iterations["10"]
weka.classifiers.meta.multisearch.RandomSearch(1)_num-slots["1"]
weka.classifiers.meta.multisearch.RandomSearch(1)_D["false"]
weka.filters.MultiFilter(weka.filters.unsupervised.attribute.ReplaceMissingValues,weka.filters.unsupervised.attribute.RemoveUseless,weka.filters.unsupervised.attribute.Normalize)(1)_F["weka.filters.unsupervised.attribute.ReplaceMissingValues","weka.filters.unsupervised.attribute.RemoveUseless","weka.filters.unsupervised.attribute.Normalize"]
weka.filters.MultiFilter(weka.filters.unsupervised.attribute.ReplaceMissingValues,weka.filters.unsupervised.attribute.RemoveUseless,weka.filters.unsupervised.attribute.Normalize)(1)_S["1"]
weka.filters.MultiFilter(weka.filters.unsupervised.attribute.ReplaceMissingValues,weka.filters.unsupervised.attribute.RemoveUseless,weka.filters.unsupervised.attribute.Normalize)(1)_output-debug-info["false"]
weka.filters.MultiFilter(weka.filters.unsupervised.attribute.ReplaceMissingValues,weka.filters.unsupervised.attribute.RemoveUseless,weka.filters.unsupervised.attribute.Normalize)(1)_-do-not-check-capabilities["false"]
weka.filters.unsupervised.attribute.RemoveUseless(1)_M["99.0"]
weka.filters.unsupervised.attribute.Normalize(1)_S["1.0"]
weka.filters.unsupervised.attribute.Normalize(1)_T["0.0"]
weka.classifiers.meta.MultiSearch(weka.classifiers.meta.multisearch.RandomSearch,weka.classifiers.meta.FilteredClassifier(weka.filters.MultiFilter(weka.filters.unsupervised.attribute.ReplaceMissingValues,weka.filters.unsupervised.attribute.RemoveUseless,weka.filters.unsupervised.attribute.Normalize),weka.classifiers.meta.LogitBoost(weka.classifiers.trees.REPTree)))(1)_E["ACC"]
weka.classifiers.meta.MultiSearch(weka.classifiers.meta.multisearch.RandomSearch,weka.classifiers.meta.FilteredClassifier(weka.filters.MultiFilter(weka.filters.unsupervised.attribute.ReplaceMissingValues,weka.filters.unsupervised.attribute.RemoveUseless,weka.filters.unsupervised.attribute.Normalize),weka.classifiers.meta.LogitBoost(weka.classifiers.trees.REPTree)))(1)_class-label["1"]
weka.classifiers.meta.MultiSearch(weka.classifiers.meta.multisearch.RandomSearch,weka.classifiers.meta.FilteredClassifier(weka.filters.MultiFilter(weka.filters.unsupervised.attribute.ReplaceMissingValues,weka.filters.unsupervised.attribute.RemoveUseless,weka.filters.unsupervised.attribute.Normalize),weka.classifiers.meta.LogitBoost(weka.classifiers.trees.REPTree)))(1)_search["weka.core.setupgenerator.MathParameter -property classifier.numIterations -min 5.0 -max 10.0 -step 1.0 -base 1.0 -expression I","weka.core.setupgenerator.MathParameter -property classifier.classifier.maxDepth -min 1.0 -max 5.0 -step 1.0 -base 1.0 -expression I","weka.core.setupgenerator.MathParameter -property classifier.shrinkage -min -4.0 -max -1.0 -step 1.0 -base 10.0 -expression pow(BASE,I)"]
weka.classifiers.meta.MultiSearch(weka.classifiers.meta.multisearch.RandomSearch,weka.classifiers.meta.FilteredClassifier(weka.filters.MultiFilter(weka.filters.unsupervised.attribute.ReplaceMissingValues,weka.filters.unsupervised.attribute.RemoveUseless,weka.filters.unsupervised.attribute.Normalize),weka.classifiers.meta.LogitBoost(weka.classifiers.trees.REPTree)))(1)_algorithm["weka.classifiers.meta.multisearch.RandomSearch"]
weka.classifiers.meta.MultiSearch(weka.classifiers.meta.multisearch.RandomSearch,weka.classifiers.meta.FilteredClassifier(weka.filters.MultiFilter(weka.filters.unsupervised.attribute.ReplaceMissingValues,weka.filters.unsupervised.attribute.RemoveUseless,weka.filters.unsupervised.attribute.Normalize),weka.classifiers.meta.LogitBoost(weka.classifiers.trees.REPTree)))(1)_log-file["/Users/janvanrijn/projects/openml-weka"]
weka.classifiers.meta.MultiSearch(weka.classifiers.meta.multisearch.RandomSearch,weka.classifiers.meta.FilteredClassifier(weka.filters.MultiFilter(weka.filters.unsupervised.attribute.ReplaceMissingValues,weka.filters.unsupervised.attribute.RemoveUseless,weka.filters.unsupervised.attribute.Normalize),weka.classifiers.meta.LogitBoost(weka.classifiers.trees.REPTree)))(1)_S["1"]
weka.classifiers.meta.MultiSearch(weka.classifiers.meta.multisearch.RandomSearch,weka.classifiers.meta.FilteredClassifier(weka.filters.MultiFilter(weka.filters.unsupervised.attribute.ReplaceMissingValues,weka.filters.unsupervised.attribute.RemoveUseless,weka.filters.unsupervised.attribute.Normalize),weka.classifiers.meta.LogitBoost(weka.classifiers.trees.REPTree)))(1)_W["weka.classifiers.meta.FilteredClassifier"]
weka.classifiers.meta.MultiSearch(weka.classifiers.meta.multisearch.RandomSearch,weka.classifiers.meta.FilteredClassifier(weka.filters.MultiFilter(weka.filters.unsupervised.attribute.ReplaceMissingValues,weka.filters.unsupervised.attribute.RemoveUseless,weka.filters.unsupervised.attribute.Normalize),weka.classifiers.meta.LogitBoost(weka.classifiers.trees.REPTree)))(1)_output-debug-info["false"]
weka.classifiers.meta.MultiSearch(weka.classifiers.meta.multisearch.RandomSearch,weka.classifiers.meta.FilteredClassifier(weka.filters.MultiFilter(weka.filters.unsupervised.attribute.ReplaceMissingValues,weka.filters.unsupervised.attribute.RemoveUseless,weka.filters.unsupervised.attribute.Normalize),weka.classifiers.meta.LogitBoost(weka.classifiers.trees.REPTree)))(1)_-do-not-check-capabilities["false"]
weka.classifiers.meta.FilteredClassifier(weka.filters.MultiFilter(weka.filters.unsupervised.attribute.ReplaceMissingValues,weka.filters.unsupervised.attribute.RemoveUseless,weka.filters.unsupervised.attribute.Normalize),weka.classifiers.meta.LogitBoost(weka.classifiers.trees.REPTree))(1)_F["weka.filters.MultiFilter"]
weka.classifiers.meta.FilteredClassifier(weka.filters.MultiFilter(weka.filters.unsupervised.attribute.ReplaceMissingValues,weka.filters.unsupervised.attribute.RemoveUseless,weka.filters.unsupervised.attribute.Normalize),weka.classifiers.meta.LogitBoost(weka.classifiers.trees.REPTree))(1)_doNotCheckForModifiedClassAttribute["false"]
weka.classifiers.meta.FilteredClassifier(weka.filters.MultiFilter(weka.filters.unsupervised.attribute.ReplaceMissingValues,weka.filters.unsupervised.attribute.RemoveUseless,weka.filters.unsupervised.attribute.Normalize),weka.classifiers.meta.LogitBoost(weka.classifiers.trees.REPTree))(1)_S["1"]
weka.classifiers.meta.FilteredClassifier(weka.filters.MultiFilter(weka.filters.unsupervised.attribute.ReplaceMissingValues,weka.filters.unsupervised.attribute.RemoveUseless,weka.filters.unsupervised.attribute.Normalize),weka.classifiers.meta.LogitBoost(weka.classifiers.trees.REPTree))(1)_W["weka.classifiers.meta.LogitBoost"]
weka.classifiers.meta.FilteredClassifier(weka.filters.MultiFilter(weka.filters.unsupervised.attribute.ReplaceMissingValues,weka.filters.unsupervised.attribute.RemoveUseless,weka.filters.unsupervised.attribute.Normalize),weka.classifiers.meta.LogitBoost(weka.classifiers.trees.REPTree))(1)_output-debug-info["false"]
weka.classifiers.meta.FilteredClassifier(weka.filters.MultiFilter(weka.filters.unsupervised.attribute.ReplaceMissingValues,weka.filters.unsupervised.attribute.RemoveUseless,weka.filters.unsupervised.attribute.Normalize),weka.classifiers.meta.LogitBoost(weka.classifiers.trees.REPTree))(1)_-do-not-check-capabilities["false"]
weka.classifiers.meta.LogitBoost(weka.classifiers.trees.REPTree)(1)_Q["false"]
weka.classifiers.meta.LogitBoost(weka.classifiers.trees.REPTree)(1)_use-estimated-priors["false"]
weka.classifiers.meta.LogitBoost(weka.classifiers.trees.REPTree)(1)_P["100"]
weka.classifiers.meta.LogitBoost(weka.classifiers.trees.REPTree)(1)_L["-1.7976931348623157E308"]
weka.classifiers.meta.LogitBoost(weka.classifiers.trees.REPTree)(1)_H["1.0"]
weka.classifiers.meta.LogitBoost(weka.classifiers.trees.REPTree)(1)_Z["3.0"]
weka.classifiers.meta.LogitBoost(weka.classifiers.trees.REPTree)(1)_O["1"]
weka.classifiers.meta.LogitBoost(weka.classifiers.trees.REPTree)(1)_E["1"]
weka.classifiers.meta.LogitBoost(weka.classifiers.trees.REPTree)(1)_resume["false"]
weka.classifiers.meta.LogitBoost(weka.classifiers.trees.REPTree)(1)_S["1"]
weka.classifiers.meta.LogitBoost(weka.classifiers.trees.REPTree)(1)_I["10"]
weka.classifiers.meta.LogitBoost(weka.classifiers.trees.REPTree)(1)_W["weka.classifiers.trees.REPTree"]
weka.classifiers.meta.LogitBoost(weka.classifiers.trees.REPTree)(1)_output-debug-info["false"]
weka.classifiers.meta.LogitBoost(weka.classifiers.trees.REPTree)(1)_-do-not-check-capabilities["false"]
weka.classifiers.trees.REPTree(1)_M["2"]
weka.classifiers.trees.REPTree(1)_V["0.001"]
weka.classifiers.trees.REPTree(1)_N["3"]
weka.classifiers.trees.REPTree(1)_S["1"]
weka.classifiers.trees.REPTree(1)_P["false"]
weka.classifiers.trees.REPTree(1)_L["-1"]
weka.classifiers.trees.REPTree(1)_I["0.0"]
weka.classifiers.trees.REPTree(1)_R["false"]
weka.classifiers.trees.REPTree(1)_output-debug-info["false"]
weka.classifiers.trees.REPTree(1)_-do-not-check-capabilities["false"]

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.

arff
Trace

ARFF file with the trace of all hyperparameter settings tried during optimization, and their performance.

19 Evaluation measures

Area under ROC curve
0.8063 ± 0.045
Per class
F measure
0.7405 ± 0.0495
Per class
Kappa
0.4182 ± 0.1124
KB relative information score
0.0889 ± 0.0803
Mean absolute error
0.4226 ± 0.0244
Mean prior absolute error
0.4545 ± 0.0011
Weighted recall
0.7487 ± 0.0465
Number of instances
768
Per class
OS information
['Eclipse Adoptium', '17.0.7', 'x86_64', 'Mac OS X', '12.5.1']
Precision
0.7414 ± 0.0502
Per class
Predictive accuracy
0.7487 ± 0.0465
Prior entropy
0.9331 ± 0.0032
Relative absolute error
0.9297 ± 0.0534
Root mean prior squared error
0.4766 ± 0.0011
Root mean squared error
0.4368 ± 0.0199
Root relative squared error
0.9164 ± 0.0415
Unweighted recall
0.6988 ± 0.0568