DEVELOPMENT... OpenML
Run
101513

Run 101513

Task 1777 (Supervised Classification) breast-cancer Uploaded 08-12-2014 by Felicia West
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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
0.5873
Per class
Build CPU time
0.0175
Build memory
717113820.8
F measure
0.6772
Per class
Kappa
0.1963
KB relative information score
332.296
Mean absolute error
0.2986
Mean prior absolute error
0.4183
Number of instances
1430
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.6728
Per class
Predictive accuracy
0.7014
Prior entropy
0.8796
Recall
0.7014
Per class
Relative absolute error
0.7138
Root mean prior squared error
0.457
Root mean squared error
0.5464
Root relative squared error
1.1956
SciMark benchmark
937.4673