DEVELOPMENT... OpenML
Run
21075

Run 21075

Task 2065 (Supervised Classification) primary-tumor Uploaded 18-06-2014 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)_E3.0
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
0.7753
Per class
F measure
0.4095
Per class
Kappa
0.3456
KB relative information score
26.1339
Mean absolute error
0.0837
Mean prior absolute error
0.0812
Number of instances
339
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.4105
Per class
Predictive accuracy
0.4218
Prior entropy
3.7542
Recall
0.4218
Per class
Relative absolute error
1.031
Root mean prior squared error
0.2011
Root mean squared error
0.2027
Root relative squared error
1.0078
SciMark benchmark
908.6779[560.12864292244511014.4538012229739483.667533998868909.90626928802961575.2331471921714]