DEVELOPMENT... OpenML
Run
147571

Run 147571

Task 3675 (Supervised Classification) pbc 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)_G0.1
weka.SMO_RBFKernel(2)_C0.01
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
0.3906
Per class
KB relative information score
36.4885
Mean absolute error
0.4498
Mean prior absolute error
0.495
Number of instances
418
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.3028
Per class
Predictive accuracy
0.5502
Prior entropy
0.9928
Recall
0.5502
Per class
Relative absolute error
0.9087
Root mean prior squared error
0.4975
Root mean squared error
0.6706
Root relative squared error
1.3481
SciMark benchmark
941.8057
UserCPU time millis
190
UserCPU time millis testing
50
UserCPU time millis training
140