Run
25216

Run 25216

Task 64 (Learning Curve) labor Uploaded 17-08-2014 by Jan van Rijn
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Flow

weka.SMO_PolyKernel(6)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(4)_C250007
weka.PolyKernel(4)_E1.0
weka.SMO_PolyKernel(6)_C1.0
weka.SMO_PolyKernel(6)_Kweka.classifiers.functions.supportVector.PolyKernel
weka.SMO_PolyKernel(6)_L0.001
weka.SMO_PolyKernel(6)_N0
weka.SMO_PolyKernel(6)_P1.0E-12
weka.SMO_PolyKernel(6)_V-1
weka.SMO_PolyKernel(6)_W1

Result files

xml
Description

XML file describing the run, including user-defined evaluation measures.

model
Model readable

A human-readable description of the model that was built.

model
Model serialized

A serialized description of the model that can be read by the tool that generated it.

arff
Predictions

ARFF file with instance-level predictions generated by the model.

21 Evaluation measures

Area under ROC curve
0.9197 ± 0.1231
Per class
Build CPU time
0.0039
Build memory
5578293829.44
F measure
0.9312 ± 0.1045
Per class
Kappa
0.8482 ± 0.2514
KB relative information score
0.8445 ± 0.2625
Mean absolute error
0.0684 ± 0.1119
Mean prior absolute error
0.457 ± 0.0208
Number of instances
570
Per class
OS information
[Oracle Corporation, 1.7.0_55, amd64, Linux, 3.8.0-44-generic]
Precision
0.9313 ± 0.0775
Per class
Predictive accuracy
0.9316 ± 0.1119
Prior entropy
0.9349 ± 0.0618
Recall
0.9316 ± 0.1119
Per class
Relative absolute error
0.1497 ± 0.2495
Root mean prior squared error
0.4773 ± 0.0219
Root mean squared error
0.2616 ± 0.2207
Root relative squared error
0.5481 ± 0.4665
SciMark benchmark
1163.1578[673.1921059718253958.2072889775735530.7670899592893955.33520857062592698.287314108989]