DEVELOPMENT... OpenML
Run
9458

Run 9458

Task 27 (Supervised Classification) colic 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)_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
0.7572
Per class
F measure
0.7743
Per class
Kappa
0.5152
KB relative information score
184.4394
Mean absolute error
0.2255
Mean prior absolute error
0.4662
Number of instances
368
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.7741
Per class
Predictive accuracy
0.7745
Prior entropy
0.9509
Recall
0.7745
Per class
Relative absolute error
0.4838
Root mean prior squared error
0.4827
Root mean squared error
0.4749
Root relative squared error
0.9839
SciMark benchmark
937.9866[559.58953556755011015.2555353338006443.69495460692616913.07556704216551758.3172656914378]