DEVELOPMENT... OpenML
Run
198269

Run 198269

Task 4561 (Supervised Classification) kdd_ipums_la_97-small 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
0.9829
Per class
F measure
0.9786
Per class
Kappa
0.9544
KB relative information score
66848.7204
Mean absolute error
0.0215
Mean prior absolute error
0.466
Number of instances
70190
Per class
OS information
[Oracle Corporation, 1.7.0_75, amd64, Linux, 3.16.0-29-generic]
Precision
0.9797
Per class
Predictive accuracy
0.9785
Prior entropy
0.9504
Recall
0.9785
Per class
Relative absolute error
0.0462
Root mean prior squared error
0.4827
Root mean squared error
0.1467
Root relative squared error
0.3039
SciMark benchmark
1336.3256
UserCPU time millis
120170
UserCPU time millis testing
25500
UserCPU time millis training
94670