DEVELOPMENT... OpenML
Run
166263

Run 166263

Task 59 (Supervised Classification) iris Uploaded 18-02-2015 by Felicia West
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Flow

weka.SMO_PolyKernel(10)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.PolyKernel(8)_E1.0
weka.PolyKernel(8)_C250007
weka.SMO_PolyKernel(10)_C1.0
weka.SMO_PolyKernel(10)_N0
weka.SMO_PolyKernel(10)_L0.001
weka.SMO_PolyKernel(10)_P1.0E-12
weka.SMO_PolyKernel(10)_V-1
weka.SMO_PolyKernel(10)_W1
weka.SMO_PolyKernel(10)_Kweka.classifiers.functions.supportVector.PolyKernel

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
0.9768
Per class
F measure
0.96
Per class
Kappa
0.94
KB relative information score
90.8539
Mean absolute error
0.2311
Mean prior absolute error
0.4444
Number of instances
150
Per class
OS information
[Oracle Corporation, 1.7.0_04, x86_64, Mac OS X, 10.10.2]
Precision
0.9605
Per class
Predictive accuracy
0.96
Prior entropy
1.585
Recall
0.96
Per class
Relative absolute error
0.52
Root mean prior squared error
0.4714
Root mean squared error
0.288
Root relative squared error
0.611
SciMark benchmark
1289.6995
UserCPU time millis
20.343
UserCPU time millis testing
1.708
UserCPU time millis training
18.635

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