DEVELOPMENT... OpenML
Run
11707

Run 11707

Task 58 (Supervised Classification) waveform-5000 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)_E1.0
weka.SMO_PolyKernel(1)_C10.0
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

Area under ROC curve
0.9314
Per class
F measure
0.865
Per class
Kappa
0.7978
KB relative information score
2730.9026
Mean absolute error
0.2522
Mean prior absolute error
0.4444
Number of instances
5000
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.8654
Per class
Predictive accuracy
0.8652
Prior entropy
1.5849
Recall
0.8652
Per class
Relative absolute error
0.5675
Root mean prior squared error
0.4714
Root mean squared error
0.3226
Root relative squared error
0.6844
SciMark benchmark
938.2662[564.29658017915111013.6533959159474483.23216931738307908.95975824819551721.1892771095258]

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