DEVELOPMENT... OpenML
Run
147710

Run 147710

Task 3668 (Supervised Classification) delta_ailerons Uploaded 03-02-2015 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)_G100.0
weka.SMO_RBFKernel(2)_C100.0
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
0.9096
Per class
F measure
0.9099
Per class
Kappa
0.8192
KB relative information score
5838.0048
Mean absolute error
0.0901
Mean prior absolute error
0.4981
Number of instances
7129
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.91
Per class
Predictive accuracy
0.9099
Prior entropy
0.9973
Recall
0.9099
Per class
Relative absolute error
0.1808
Root mean prior squared error
0.4991
Root mean squared error
0.3001
Root relative squared error
0.6013
SciMark benchmark
917.7039
UserCPU time millis
231920
UserCPU time millis testing
2450
UserCPU time millis training
229470