Run
25225

Run 25225

Task 73 (Learning Curve) mfeat-fourier Uploaded 17-08-2014 by Jan van Rijn
0 likes downloaded by 0 people 0 issues 0 downvotes , 0 total downloads
Issue #Downvotes for this reason By


Flow

weka.SMO_PolyKernel(6)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(4)_C250007
weka.PolyKernel(4)_E1.0
weka.SMO_PolyKernel(6)_C1.0
weka.SMO_PolyKernel(6)_Kweka.classifiers.functions.supportVector.PolyKernel
weka.SMO_PolyKernel(6)_L0.001
weka.SMO_PolyKernel(6)_N0
weka.SMO_PolyKernel(6)_P1.0E-12
weka.SMO_PolyKernel(6)_V-1
weka.SMO_PolyKernel(6)_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.

21 Evaluation measures

Area under ROC curve
0.9699 ± 0.0059
Per class
Build CPU time
0.2816
Build memory
4633284497.12
F measure
0.8384 ± 0.0223
Per class
Kappa
0.8214 ± 0.0243
KB relative information score
0.2882 ± 0.0024
Mean absolute error
0.1611 ± 0.0002
Mean prior absolute error
0.18
Number of instances
20000
Per class
OS information
[Oracle Corporation, 1.7.0_55, amd64, Linux, 3.8.0-44-generic]
Precision
0.8382 ± 0.0228
Per class
Predictive accuracy
0.8393 ± 0.0218
Prior entropy
3.3219
Recall
0.8393 ± 0.0218
Per class
Relative absolute error
0.8948 ± 0.0011
Root mean prior squared error
0.3
Root mean squared error
0.2739 ± 0.0004
Root relative squared error
0.9129 ± 0.0012
SciMark benchmark
1162.9383[686.7089236991601961.0747621016069534.3328239521146961.99636394243022670.5788588263827]