DEVELOPMENT... OpenML
Run
151599

Run 151599

Task 3758 (Supervised Classification) chscase_geyser1 Uploaded 09-02-2015 by Felicia West
0 likes downloaded by 0 people 0 issues 0 downvotes , 0 total downloads
Issue #Downvotes for this reason By


Flow

weka.IterativeClassifierOptimizer_LogitBoost_DecisionStump(1)Weka implementation of IterativeClassifierOptimizer
weka.LogitBoost_DecisionStump(2)_E1
weka.LogitBoost_DecisionStump(2)_H1.0
weka.LogitBoost_DecisionStump(2)_I10
weka.LogitBoost_DecisionStump(2)_L-1.7976931348623157E308
weka.LogitBoost_DecisionStump(2)_O1
weka.LogitBoost_DecisionStump(2)_P100
weka.LogitBoost_DecisionStump(2)_S1
weka.LogitBoost_DecisionStump(2)_Wweka.classifiers.trees.DecisionStump
weka.LogitBoost_DecisionStump(2)_Z3.0
weka.IterativeClassifierOptimizer_LogitBoost_DecisionStump(1)_L50
weka.IterativeClassifierOptimizer_LogitBoost_DecisionStump(1)_P1
weka.IterativeClassifierOptimizer_LogitBoost_DecisionStump(1)_E1
weka.IterativeClassifierOptimizer_LogitBoost_DecisionStump(1)_I1
weka.IterativeClassifierOptimizer_LogitBoost_DecisionStump(1)_F10
weka.IterativeClassifierOptimizer_LogitBoost_DecisionStump(1)_R1
weka.IterativeClassifierOptimizer_LogitBoost_DecisionStump(1)_Wweka.classifiers.meta.LogitBoost
weka.IterativeClassifierOptimizer_LogitBoost_DecisionStump(1)_metricRMSE
weka.IterativeClassifierOptimizer_LogitBoost_DecisionStump(1)_S1

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
0.9088
Per class
F measure
0.8759
Per class
Kappa
0.7382
KB relative information score
142.6176
Mean absolute error
0.1824
Mean prior absolute error
0.4787
Number of instances
222
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.8823
Per class
Predictive accuracy
0.8784
Prior entropy
0.9694
Recall
0.8784
Per class
Relative absolute error
0.3809
Root mean prior squared error
0.4891
Root mean squared error
0.3146
Root relative squared error
0.6431
SciMark benchmark
911.0478
UserCPU time millis
10
UserCPU time millis training
10