OpenML
1029

Run 1029

Task 99 (Learning Curve) glass Uploaded 09-04-2014 by Jan van Rijn
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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.

17 Evaluation measures

0.7518 ± 0.0632
Per class
0.3878 ± 0.1394
0.1309 ± 0.0293
0.2134 ± 0.0023
0.2116 ± 0.0025
2140
Per class
[ Oracle Corporation, 1.7.0_15, amd64, Linux, 3.5.0-27-generic ]
0.5813 ± 0.0948
2.1835 ± 0.0748
0.5813 ± 0.0948
Per class
1.0086 ± 0.013
0.3244 ± 0.0039
0.3161 ± 0.0036
0.9743 ± 0.0129
879.407