DEVELOPMENT... OpenML
Run
13536

Run 13536

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)_E2.0
weka.SMO_PolyKernel(1)_C0.01
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
0.9307
Per class
F measure
0.8636
Per class
Kappa
0.7961
KB relative information score
2727.6759
Mean absolute error
0.2524
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
0.865
Per class
Predictive accuracy
0.864
Prior entropy
1.5849
Recall
0.864
Per class
Relative absolute error
0.568
Root mean prior squared error
0.4714
Root mean squared error
0.3229
Root relative squared error
0.6851
SciMark benchmark
945.7823[554.4312430895411013.9200573100866483.23216931738307909.90626928802961767.4218398162996]