DEVELOPMENT... OpenML
Run
16487

Run 16487

Task 2073 (Supervised Classification) yeast 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)_C10.0
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
0.7992
Per class
F measure
0.5922
Per class
Kappa
0.4791
KB relative information score
183.9734
Mean absolute error
0.1631
Mean prior absolute error
0.1555
Number of instances
1484
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.6015
Per class
Predictive accuracy
0.6018
Prior entropy
2.5035
Recall
0.6018
Per class
Relative absolute error
1.0487
Root mean prior squared error
0.2787
Root mean squared error
0.2777
Root relative squared error
0.9963
SciMark benchmark
948.1584[566.4958240211141007.8216252346145484.3219757802961912.43998791392861769.7127006129608]