DEVELOPMENT... OpenML
Run
143577

Run 143577

Task 3885 (Supervised Classification) mfeat-pixel Uploaded 31-01-2015 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.

22 Evaluation measures

Area under ROC curve
0.9697
Per class
F measure
0.9934
Per class
Kappa
0.963
KB relative information score
1898.7308
Mean absolute error
0.0065
Mean prior absolute error
0.1803
Number of instances
2000
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.9935
Per class
Predictive accuracy
0.9935
Prior entropy
0.4703
Recall
0.9935
Per class
Relative absolute error
0.036
Root mean prior squared error
0.3
Root mean squared error
0.0806
Root relative squared error
0.2687
SciMark benchmark
917.7039
UserCPU time millis
35620
UserCPU time millis testing
13650
UserCPU time millis training
21970