DEVELOPMENT... OpenML
Run
197735

Run 197735

Task 4396 (Supervised Classification) sensory Uploaded 26-02-2015 by Felicia West
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Flow

weka.SMO_RBFKernel(3)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(3)_G0.01
weka.RBFKernel(3)_C250007
weka.SMO_RBFKernel(3)_C1.0
weka.SMO_RBFKernel(3)_N0
weka.SMO_RBFKernel(3)_L0.001
weka.SMO_RBFKernel(3)_P1.0E-12
weka.SMO_RBFKernel(3)_V-1
weka.SMO_RBFKernel(3)_W1
weka.SMO_RBFKernel(3)_Kweka.classifiers.functions.supportVector.RBFKernel

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
0.5678
Per class
F measure
0.5723
Per class
Kappa
0.1494
KB relative information score
1256.4675
Mean absolute error
0.3748
Mean prior absolute error
0.4856
Number of instances
5760
Per class
OS information
[Oracle Corporation, 1.7.0_75, amd64, Linux, 3.16.0-29-generic]
Precision
0.6275
Per class
Predictive accuracy
0.6252
Prior entropy
0.9792
Recall
0.6252
Per class
Relative absolute error
0.7719
Root mean prior squared error
0.4927
Root mean squared error
0.6122
Root relative squared error
1.2426
SciMark benchmark
1320.2556
UserCPU time millis
180
UserCPU time millis testing
70
UserCPU time millis training
110