DEVELOPMENT... OpenML
Run
15969

Run 15969

Task 2079 (Supervised Classification) eucalyptus Uploaded 04-06-2014 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)_C100.0
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
0.8676
Per class
F measure
0.6201
Per class
Kappa
0.5183
KB relative information score
238.2235
Mean absolute error
0.2596
Mean prior absolute error
0.3132
Number of instances
736
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.6189
Per class
Predictive accuracy
0.6223
Prior entropy
2.2629
Recall
0.6223
Per class
Relative absolute error
0.8287
Root mean prior squared error
0.3957
Root mean squared error
0.3448
Root relative squared error
0.8713
SciMark benchmark
942.2395[560.48863959327881011.5249537742577481.93081680843505907.38665814554741749.8664669073971]