DEVELOPMENT... OpenML
Run
15306

Run 15306

Task 1742 (Learning Curve) spambase Uploaded 03-06-2014 by Mandy
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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)_E1.0
weka.SMO_PolyKernel(1)_C1.0
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

0.8896 ± 0.0156
Per class
0.9016 ± 0.0142
Per class
0.7926 ± 0.0301
0.792 ± 0.0299
0.0974 ± 0.014
0.4776 ± 0.0002
4601
Per class
[ Oracle Corporation, 1.7.0_15, amd64, Linux, 3.5.0-27-generic ]
0.9035 ± 0.0143
Per class
0.9026 ± 0.014
0.9674 ± 0.0006
0.9026 ± 0.014
Per class
0.2039 ± 0.0293
0.4886 ± 0.0002
0.312 ± 0.0225
0.6386 ± 0.0462
883.3113