Run
55848

Run 55848

Task 1731 (Learning Curve) pendigits Uploaded 12-11-2014 by Jan van Rijn
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  • curves study_2 study_37 weka weka_3.7.12-SNAPSHOT
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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
0.9968 ± 0.0006
Per class
Build CPU time
0.2806
Build memory
2213282245.5
F measure
0.9794 ± 0.0042
Per class
Kappa
0.9772 ± 0.0047
KB relative information score
0.2991 ± 0.0003
Mean absolute error
0.1601 ± 0
Mean prior absolute error
0.18 ± 0
Number of instances
10992
Per class
OS information
[Sun Microsystems Inc., 1.6.0_33, amd64, Linux, 3.5.0-54-generic]
Precision
0.9795 ± 0.0042
Per class
Predictive accuracy
0.9794 ± 0.0042
Prior entropy
3.3208 ± 0.0002
Recall
0.9794 ± 0.0042
Per class
Relative absolute error
0.8899 ± 0.0002
Root mean prior squared error
0.3 ± 0
Root mean squared error
0.272 ± 0.0001
Root relative squared error
0.9066 ± 0.0002
SciMark benchmark
1614.7273