DEVELOPMENT... OpenML
Run
16897

Run 16897

Task 2074 (Supervised Classification) satimage Uploaded 05-06-2014 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)_C0.1
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
0.9631
Per class
F measure
0.8757
Per class
Kappa
0.8506
KB relative information score
2201.9548
Mean absolute error
0.2255
Mean prior absolute error
0.2701
Number of instances
6430
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.8741
Per class
Predictive accuracy
0.8795
Prior entropy
2.4836
Recall
0.8795
Per class
Relative absolute error
0.8348
Root mean prior squared error
0.3675
Root mean squared error
0.3149
Root relative squared error
0.8568
SciMark benchmark
938.0267[537.18529450896091013.6533323510344484.10363182779514906.44537908266061748.7457828491113]