DEVELOPMENT... OpenML
Run
198002

Run 198002

Task 206 (Supervised Classification) BNG(tic-tac-toe) Uploaded 27-02-2015 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
0.6959
Per class
F measure
0.7362
Per class
Kappa
0.4085
KB relative information score
16088.4696
Mean absolute error
0.2572
Mean prior absolute error
0.4532
Number of instances
39366
Per class
OS information
[Oracle Corporation, 1.7.0_75, amd64, Linux, 3.16.0-29-generic]
Precision
0.7355
Per class
Predictive accuracy
0.7428
Prior entropy
0.9315
Recall
0.7428
Per class
Relative absolute error
0.5674
Root mean prior squared error
0.476
Root mean squared error
0.5071
Root relative squared error
1.0653
SciMark benchmark
1313.9994
UserCPU time millis
5486080
UserCPU time millis testing
564960
UserCPU time millis training
4921120