DEVELOPMENT... OpenML
Run
102401

Run 102401

Task 3734 (Supervised Classification) fri_c1_500_5 Uploaded 08-12-2014 by Felicia West
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Flow

weka.SMO_RBFKernel(2)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.RBFKernel(2)_C250007
weka.RBFKernel(2)_G0.01
weka.SMO_RBFKernel(2)_C1.0
weka.SMO_RBFKernel(2)_Kweka.classifiers.functions.supportVector.RBFKernel
weka.SMO_RBFKernel(2)_L0.001
weka.SMO_RBFKernel(2)_N0
weka.SMO_RBFKernel(2)_P1.0E-12
weka.SMO_RBFKernel(2)_V-1
weka.SMO_RBFKernel(2)_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
0.5043
Per class
Build CPU time
0.1324
Build memory
1213098090.4
F measure
0.3807
Per class
Kappa
0.0092
KB relative information score
34.9143
Mean absolute error
0.462
Mean prior absolute error
0.4977
Number of instances
500
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.7523
Per class
Predictive accuracy
0.538
Prior entropy
0.9967
Recall
0.538
Per class
Relative absolute error
0.9283
Root mean prior squared error
0.4988
Root mean squared error
0.6797
Root relative squared error
1.3626
SciMark benchmark
940.4464