DEVELOPMENT... OpenML
Run
197648

Run 197648

Task 4311 (Supervised Classification) fri_c3_1000_10 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
0.5002
Per class
F measure
0.4074
Per class
Kappa
0.0005
KB relative information score
1002.4429
Mean absolute error
0.4406
Mean prior absolute error
0.4928
Number of instances
10000
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.5148
Per class
Predictive accuracy
0.5594
Prior entropy
0.9896
Recall
0.5594
Per class
Relative absolute error
0.894
Root mean prior squared error
0.4964
Root mean squared error
0.6638
Root relative squared error
1.3372
SciMark benchmark
940.3347
UserCPU time millis
1170
UserCPU time millis testing
150
UserCPU time millis training
1020