DEVELOPMENT... OpenML
Run
197627

Run 197627

Task 4289 (Supervised Classification) fri_c4_1000_100 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
0.5399
Per class
F measure
0.4953
Per class
Kappa
0.0884
KB relative information score
1703.438
Mean absolute error
0.4051
Mean prior absolute error
0.4918
Number of instances
10000
Per class
OS information
[Oracle Corporation, 1.7.0_75, amd64, Linux, 3.16.0-29-generic]
Precision
0.6508
Per class
Predictive accuracy
0.5949
Prior entropy
0.9882
Recall
0.5949
Per class
Relative absolute error
0.8237
Root mean prior squared error
0.4959
Root mean squared error
0.6365
Root relative squared error
1.2835
SciMark benchmark
1034.1273
UserCPU time millis
1480
UserCPU time millis testing
610
UserCPU time millis training
870