Run
437779

Run 437779

Task 7536 (Learning Curve) monks-problems-3 Uploaded 13-05-2015 by Joaquin Vanschoren
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Flow

weka.SMO_PolyKernel(10)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(8)_E1.0
weka.PolyKernel(8)_C250007
weka.SMO_PolyKernel(10)_C1.0
weka.SMO_PolyKernel(10)_N0
weka.SMO_PolyKernel(10)_L0.001
weka.SMO_PolyKernel(10)_P1.0E-12
weka.SMO_PolyKernel(10)_V-1
weka.SMO_PolyKernel(10)_W1
weka.SMO_PolyKernel(10)_Kweka.classifiers.functions.supportVector.PolyKernel

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.

22 Evaluation measures

Area under ROC curve
0.975 ± 0.0182
Per class
F measure
0.9747 ± 0.0193
Per class
Kappa
0.9494 ± 0.0383
KB relative information score
0.9493 ± 0.0385
Mean absolute error
0.0253 ± 0.0192
Mean prior absolute error
0.4992 ± 0.0003
Number of instances
554
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.9748 ± 0.0166
Per class
Predictive accuracy
0.9747 ± 0.0192
Prior entropy
0.9989 ± 0.0008
Recall
0.9747 ± 0.0192
Per class
Relative absolute error
0.0506 ± 0.0385
Root mean prior squared error
0.4996 ± 0.0003
Root mean squared error
0.159 ± 0.0673
Root relative squared error
0.3182 ± 0.1348
SciMark benchmark
941.3619
UserCPU time millis
20
UserCPU time millis training
20