DEVELOPMENT... OpenML
Run
197060

Run 197060

Task 3564 (Supervised Classification) analcatdata_germangss 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
0.4475
Per class
F measure
0.1938
Per class
Kappa
-0.0733
KB relative information score
27.4115
Mean absolute error
0.3842
Mean prior absolute error
0.375
Number of instances
400
Per class
OS information
[Oracle Corporation, 1.7.0_75, amd64, Linux, 3.16.0-29-generic]
Precision
0.2983
Per class
Predictive accuracy
0.195
Prior entropy
2
Recall
0.195
Per class
Relative absolute error
1.0244
Root mean prior squared error
0.433
Root mean squared error
0.477
Root relative squared error
1.1017
SciMark benchmark
1337.7959
UserCPU time millis
130
UserCPU time millis testing
70
UserCPU time millis training
60

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