Run
504099

Run 504099

Task 1764 (Learning Curve) electricity Uploaded 30-12-2015 by Joaquin Vanschoren
0 likes downloaded by 0 people 0 issues 0 downvotes , 0 total downloads
Issue #Downvotes for this reason By


Flow

weka.AttributeSelectedClassifier_CfsSubsetEval_BestFirst_Bagging_REPTree(3)Weka implementation of AttributeSelectedClassifier
weka.REPTree(15)_M2
weka.REPTree(15)_V0.001
weka.REPTree(15)_N16
weka.REPTree(15)_S1
weka.REPTree(15)_L-1
weka.REPTree(15)_I0.0
weka.REPTree(15)_num-decimal-places4
weka.CfsSubsetEval(5)_P1
weka.CfsSubsetEval(5)_E1
weka.BestFirst(4)_D1
weka.BestFirst(4)_N5
weka.AttributeSelectedClassifier_CfsSubsetEval_BestFirst_Bagging_REPTree(3)_Eweka.attributeSelection.CfsSubsetEval
weka.AttributeSelectedClassifier_CfsSubsetEval_BestFirst_Bagging_REPTree(3)_Sweka.attributeSelection.BestFirst
weka.AttributeSelectedClassifier_CfsSubsetEval_BestFirst_Bagging_REPTree(3)_Wweka.classifiers.meta.Bagging
weka.AttributeSelectedClassifier_CfsSubsetEval_BestFirst_Bagging_REPTree(3)_num-decimal-places4
weka.Bagging_REPTree(19)_P100
weka.Bagging_REPTree(19)_S1
weka.Bagging_REPTree(19)_num-slots4
weka.Bagging_REPTree(19)_I10
weka.Bagging_REPTree(19)_Wweka.classifiers.trees.REPTree
weka.Bagging_REPTree(19)_num-decimal-places4

Result files

xml
Description

XML file describing the run, including user-defined evaluation measures.

model
Model readable

A human-readable description of the model that was built.

model
Model serialized

A serialized description of the model that can be read by the tool that generated it.

arff
Predictions

ARFF file with instance-level predictions generated by the model.

22 Evaluation measures

Area under ROC curve
0.8826 ± 0.0042
Per class
F measure
0.799 ± 0.0039
Per class
Kappa
0.5873 ± 0.0081
KB relative information score
21194.4889 ± 24.2946
Mean absolute error
0.2709 ± 0.0026
Mean prior absolute error
0.4886 ± 0
Number of instances
45312
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.7992 ± 0.0038
Per class
Predictive accuracy
0.8 ± 0.0037
Prior entropy
0.9835
Recall
0.8 ± 0.0037
Per class
Relative absolute error
0.5544 ± 0.0053
Root mean prior squared error
0.4943 ± 0
Root mean squared error
0.3706 ± 0.0033
Root relative squared error
0.7499 ± 0.0067
SciMark benchmark
945.1219
UserCPU time millis
790
UserCPU time millis testing
260
UserCPU time millis training
530