DEVELOPMENT... OpenML
Run
19464

Run 19464

Task 2072 (Supervised Classification) covertype Uploaded 15-06-2014 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)_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
0.8101
Per class
F measure
0.6873
Per class
Kappa
0.5367
KB relative information score
8228.5207
Mean absolute error
0.2101
Mean prior absolute error
0.1853
Number of instances
110393
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.7132
Per class
Predictive accuracy
0.7124
Prior entropy
1.8698
Recall
0.7124
Per class
Relative absolute error
1.1341
Root mean prior squared error
0.3044
Root mean squared error
0.311
Root relative squared error
1.0219
SciMark benchmark
905.506[537.5163949195284979.8906509093104461.42753282563893874.97995825765031673.7153889608276]