DEVELOPMENT... OpenML
Run
143353

Run 143353

Task 3653 (Supervised Classification) triazines Uploaded 31-01-2015 by Felicia West
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Flow

weka.SMO_RBFKernel(2)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(2)_C250007
weka.RBFKernel(2)_G0.01
weka.SMO_RBFKernel(2)_C1.0
weka.SMO_RBFKernel(2)_Kweka.classifiers.functions.supportVector.RBFKernel
weka.SMO_RBFKernel(2)_L0.001
weka.SMO_RBFKernel(2)_N0
weka.SMO_RBFKernel(2)_P1.0E-12
weka.SMO_RBFKernel(2)_V-1
weka.SMO_RBFKernel(2)_W1

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
0.5
Per class
F measure
0.4331
Per class
KB relative information score
25.2741
Mean absolute error
0.414
Mean prior absolute error
0.4854
Number of instances
186
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.3434
Per class
Predictive accuracy
0.586
Prior entropy
0.979
Recall
0.586
Per class
Relative absolute error
0.8529
Root mean prior squared error
0.4925
Root mean squared error
0.6434
Root relative squared error
1.3063
SciMark benchmark
917.7039
UserCPU time millis
50
UserCPU time millis testing
20
UserCPU time millis training
30