DEVELOPMENT... OpenML
Run
195349

Run 195349

Task 271 (Supervised Classification) soybean Uploaded 26-02-2015 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
0.9854
Per class
F measure
0.9224
Per class
Kappa
0.9163
KB relative information score
39.1825
Mean absolute error
0.0942
Mean prior absolute error
0.0958
Number of instances
225
Per class
OS information
[Oracle Corporation, 1.7.0_75, amd64, Linux, 3.16.0-29-generic]
Precision
0.9243
Per class
Predictive accuracy
0.9244
Prior entropy
3.8572
Recall
0.9244
Per class
Relative absolute error
0.9834
Root mean prior squared error
0.2185
Root mean squared error
0.213
Root relative squared error
0.975
SciMark benchmark
1369.9744
UserCPU time millis
820
UserCPU time millis testing
50
UserCPU time millis training
770

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