DEVELOPMENT... OpenML
Run
196450

Run 196450

Task 4543 (Supervised Classification) autos Uploaded 26-02-2015 by Felicia West
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Flow

weka.SMO_PolyKernel(10)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.PolyKernel(8)_E1.0
weka.PolyKernel(8)_C250007
weka.SMO_PolyKernel(10)_C1.0
weka.SMO_PolyKernel(10)_N0
weka.SMO_PolyKernel(10)_L0.001
weka.SMO_PolyKernel(10)_P1.0E-12
weka.SMO_PolyKernel(10)_V-1
weka.SMO_PolyKernel(10)_W1
weka.SMO_PolyKernel(10)_Kweka.classifiers.functions.supportVector.PolyKernel

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
0.7947
Per class
F measure
0.8237
Per class
Kappa
0.5969
KB relative information score
1189.0716
Mean absolute error
0.1751
Mean prior absolute error
0.4406
Number of instances
2050
Per class
OS information
[Oracle Corporation, 1.7.0_75, amd64, Linux, 3.16.0-29-generic]
Precision
0.823
Per class
Predictive accuracy
0.8249
Prior entropy
0.9134
Recall
0.8249
Per class
Relative absolute error
0.3975
Root mean prior squared error
0.4691
Root mean squared error
0.4185
Root relative squared error
0.8922
SciMark benchmark
1287.514
UserCPU time millis
20
UserCPU time millis training
20