DEVELOPMENT... OpenML
Run
10325

Run 10325

Task 37 (Supervised Classification) diabetes 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)_E3.0
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
0.6079
Per class
F measure
0.664
Per class
Kappa
0.2558
KB relative information score
266.2907
Mean absolute error
0.2852
Mean prior absolute error
0.4545
Number of instances
768
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.7326
Per class
Predictive accuracy
0.7148
Prior entropy
0.9335
Recall
0.7148
Per class
Relative absolute error
0.6274
Root mean prior squared error
0.4766
Root mean squared error
0.534
Root relative squared error
1.1203
SciMark benchmark
947.9269[564.114047767191016.3264661877167480.2065271424506909.27509318503251769.7125638882815]