DEVELOPMENT... OpenML
Run
197707

Run 197707

Task 4368 (Supervised Classification) cholesterol 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
0.4974
Per class
F measure
0.3951
Per class
Kappa
-0.0057
KB relative information score
229.3394
Mean absolute error
0.4561
Mean prior absolute error
0.4955
Number of instances
3030
Per class
OS information
[Oracle Corporation, 1.7.0_75, amd64, Linux, 3.16.0-29-generic]
Precision
0.4638
Per class
Predictive accuracy
0.5439
Prior entropy
0.9935
Recall
0.5439
Per class
Relative absolute error
0.9206
Root mean prior squared error
0.4977
Root mean squared error
0.6754
Root relative squared error
1.3569
SciMark benchmark
1336.3256
UserCPU time millis
80
UserCPU time millis testing
20
UserCPU time millis training
60