DEVELOPMENT... OpenML
Run
8316

Run 8316

Task 16 (Supervised Classification) mfeat-karhunen Uploaded 22-05-2014 by Felicia West
0 likes downloaded by 0 people 0 issues 0 downvotes , 0 total downloads
Issue #Downvotes for this reason By


Flow

weka.SMO_PolyKernel(1)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(1)_C250007
weka.PolyKernel(1)_E1.0
weka.SMO_PolyKernel(1)_C0.01
weka.SMO_PolyKernel(1)_Kweka.classifiers.functions.supportVector.PolyKernel
weka.SMO_PolyKernel(1)_L0.001
weka.SMO_PolyKernel(1)_N0
weka.SMO_PolyKernel(1)_P1.0E-12
weka.SMO_PolyKernel(1)_V-1
weka.SMO_PolyKernel(1)_W1

Result files

xml
Description

XML file describing the run, including user-defined evaluation measures.

arff
Predictions

ARFF file with instance-level predictions generated by the model.

19 Evaluation measures

Area under ROC curve
0.9741
Per class
F measure
0.8763
Per class
Kappa
0.8639
KB relative information score
577.8413
Mean absolute error
0.161
Mean prior absolute error
0.18
Number of instances
2000
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.8793
Per class
Predictive accuracy
0.8775
Prior entropy
3.3219
Recall
0.8775
Per class
Relative absolute error
0.8945
Root mean prior squared error
0.3
Root mean squared error
0.2739
Root relative squared error
0.9131
SciMark benchmark
949.2752[566.4958240211141015.2555353338006489.39918924322484916.90802019261121758.3172656914378]