DEVELOPMENT... OpenML
Run
196631

Run 196631

Task 16 (Supervised Classification) mfeat-karhunen Uploaded 26-02-2015 by Jason
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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
0.982
Per class
F measure
0.927
Per class
Kappa
0.9189
KB relative information score
585.612
Mean absolute error
0.1607
Mean prior absolute error
0.18
Number of instances
2000
Per class
OS information
[Oracle Corporation, 1.7.0_75, amd64, Linux, 3.16.0-29-generic]
Precision
0.9283
Per class
Predictive accuracy
0.927
Prior entropy
3.3219
Recall
0.927
Per class
Relative absolute error
0.8927
Root mean prior squared error
0.3
Root mean squared error
0.2734
Root relative squared error
0.9113
SciMark benchmark
1363.434
UserCPU time millis
13080
UserCPU time millis testing
10660
UserCPU time millis training
2420