DEVELOPMENT... OpenML
Run
13608

Run 13608

Task 47 (Supervised Classification) tae Uploaded 22-05-2014 by Felicia West
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Flow

weka.SMO_PolyKernel(1)J. Platt: Fast Training of Support Vector Machines using Sequential Minimal Optimization. In B. Schoelkopf and C. Burges and A. Smola, editors, Advances in Kernel Methods - Support Vector Learning, 1998. S.S. Keerthi, S.K. Shevade, C. Bhattacharyya, K.R.K. Murthy (2001). Improvements to Platt's SMO Algorithm for SVM Classifier Design. Neural Computation. 13(3):637-649. Trevor Hastie, Robert Tibshirani: Classification by Pairwise Coupling. In: Advances in Neural Information Processing Systems, 1998.
weka.PolyKernel(1)_C250007
weka.PolyKernel(1)_E2.0
weka.SMO_PolyKernel(1)_C0.1
weka.SMO_PolyKernel(1)_Kweka.classifiers.functions.supportVector.PolyKernel
weka.SMO_PolyKernel(1)_L0.001
weka.SMO_PolyKernel(1)_N0
weka.SMO_PolyKernel(1)_P1.0E-12
weka.SMO_PolyKernel(1)_V-1
weka.SMO_PolyKernel(1)_W1

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.

19 Evaluation measures

Area under ROC curve
0.6501
Per class
F measure
0.5174
Per class
Kappa
0.2964
KB relative information score
39.2253
Mean absolute error
0.3723
Mean prior absolute error
0.4443
Number of instances
151
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.5302
Per class
Predictive accuracy
0.5298
Prior entropy
1.5845
Recall
0.5298
Per class
Relative absolute error
0.838
Root mean prior squared error
0.4713
Root mean squared error
0.4735
Root relative squared error
1.0046
SciMark benchmark
943.0303[562.29550002336991014.7208839634673478.0684770170223912.43998791392861747.6266666666663]