DEVELOPMENT... OpenML
Run
197851

Run 197851

Task 2980 (Supervised Classification) meta_stream_intervals.arff Uploaded 27-02-2015 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
0.9794
Per class
F measure
0.9409
Per class
Kappa
0.9164
KB relative information score
22112.4162
Mean absolute error
0.13
Mean prior absolute error
0.1072
Number of instances
451640
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.942
Per class
Predictive accuracy
0.9422
Prior entropy
2.3329
Recall
0.9422
Per class
Relative absolute error
1.2132
Root mean prior squared error
0.2315
Root mean squared error
0.2479
Root relative squared error
1.0709
SciMark benchmark
915.9324
UserCPU time millis
595650
UserCPU time millis testing
3250
UserCPU time millis training
592400