Run
4605

Run 4605

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.trees.RandomForest))(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.trees.RandomForest))(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.trees.RandomForest))(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.trees.RandomForest))(1)_search["weka.core.setupgenerator.MathParameter -property classifier.numFeatures -min 0.1 -max 0.9 -step 0.1 -base 1.0 -expression 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.trees.RandomForest))(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.trees.RandomForest))(1)_log-file["/Users/janvanrijn/projects/openml-weka/target/checkout"]
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.trees.RandomForest))(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.trees.RandomForest))(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.trees.RandomForest))(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.trees.RandomForest))(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.trees.RandomForest)(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.trees.RandomForest)(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.trees.RandomForest)(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.trees.RandomForest)(1)_W["weka.classifiers.trees.RandomForest"]
weka.classifiers.meta.FilteredClassifier(weka.filters.MultiFilter(weka.filters.unsupervised.attribute.ReplaceMissingValues,weka.filters.unsupervised.attribute.RemoveUseless,weka.filters.unsupervised.attribute.Normalize),weka.classifiers.trees.RandomForest)(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.trees.RandomForest)(1)_-do-not-check-capabilities["false"]
weka.classifiers.trees.RandomForest(1)_P["100"]
weka.classifiers.trees.RandomForest(1)_O["false"]
weka.classifiers.trees.RandomForest(1)_store-out-of-bag-predictions["false"]
weka.classifiers.trees.RandomForest(1)_output-out-of-bag-complexity-statistics["false"]
weka.classifiers.trees.RandomForest(1)_print["false"]
weka.classifiers.trees.RandomForest(1)_attribute-importance["false"]
weka.classifiers.trees.RandomForest(1)_I["100"]
weka.classifiers.trees.RandomForest(1)_num-slots["1"]
weka.classifiers.trees.RandomForest(1)_K["0"]
weka.classifiers.trees.RandomForest(1)_M["1.0"]
weka.classifiers.trees.RandomForest(1)_V["0.001"]
weka.classifiers.trees.RandomForest(1)_S["1"]
weka.classifiers.trees.RandomForest(1)_U["false"]
weka.classifiers.trees.RandomForest(1)_B["false"]
weka.classifiers.trees.RandomForest(1)_output-debug-info["false"]
weka.classifiers.trees.RandomForest(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
0.8303 ± 0.0508
Per class
F measure
0.7681 ± 0.0465
Per class
Kappa
0.4849 ± 0.104
KB relative information score
0.3362 ± 0.0737
Mean absolute error
0.3052 ± 0.028
Mean prior absolute error
0.4545 ± 0.0011
Weighted recall
0.7708 ± 0.0455
Number of instances
768
Per class
OS information
['Homebrew', '23', 'aarch64', 'Mac OS X', '12.5.1']
Precision
0.7671 ± 0.047
Per class
Predictive accuracy
0.7708 ± 0.0455
Prior entropy
0.9331 ± 0.0032
Relative absolute error
0.6716 ± 0.0617
Root mean prior squared error
0.4766 ± 0.0011
Root mean squared error
0.398 ± 0.0335
Root relative squared error
0.8349 ± 0.0709
Unweighted recall
0.7374 ± 0.0514