DEVELOPMENT... OpenML
Run
7298

Run 7298

Task 29 (Supervised Classification) credit-approval 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)_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

Area under ROC curve
0.8533
Per class
F measure
0.8538
Per class
Kappa
0.7045
KB relative information score
484.4043
Mean absolute error
0.1464
Mean prior absolute error
0.494
Number of instances
690
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.8543
Per class
Predictive accuracy
0.8536
Prior entropy
0.9913
Recall
0.8536
Per class
Relative absolute error
0.2963
Root mean prior squared error
0.497
Root mean squared error
0.3826
Root relative squared error
0.7699
SciMark benchmark
947.746[570.57263610873451013.3868112175512483.0148066605872911.17140496421141760.5845701777503]