DEVELOPMENT... OpenML
Run
25830

Run 25830

Task 59 (Supervised Classification) iris Uploaded 07-10-2014 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)_G10.0
weka.SMO_RBFKernel(2)_C0.01
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
0.8698
Per class
Build CPU time
0.0121
Build memory
234647256.8
F measure
0.7737
Per class
Kappa
0.67
KB relative information score
69.557
Mean absolute error
0.283
Mean prior absolute error
0.4444
Number of instances
150
Per class
OS information
[Oracle Corporation, 1.7.0_67, x86_64, Mac OS X, 10.9.5]
Precision
0.7934
Per class
Predictive accuracy
0.78
Prior entropy
1.585
Recall
0.78
Per class
Relative absolute error
0.6367
Root mean prior squared error
0.4714
Root mean squared error
0.3576
Root relative squared error
0.7587
SciMark benchmark
1571.9202[1030.87506356800871238.402965993701659.54656643867461306.79957386067173623.9770696272967]