Run
102158

Run 102158

Task 2124 (Supervised Classification) braziltourism Uploaded 08-12-2014 by Joaquin Vanschoren
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Flow

weka.SMO_RBFKernel(2)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(2)_C250007
weka.RBFKernel(2)_G0.01
weka.SMO_RBFKernel(2)_C1.0
weka.SMO_RBFKernel(2)_Kweka.classifiers.functions.supportVector.RBFKernel
weka.SMO_RBFKernel(2)_L0.001
weka.SMO_RBFKernel(2)_N0
weka.SMO_RBFKernel(2)_P1.0E-12
weka.SMO_RBFKernel(2)_V-1
weka.SMO_RBFKernel(2)_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
0.5
Per class
Cross-validation details (33% Holdout set)
Build CPU time
0.08
Cross-validation details (33% Holdout set)
Build memory
1160566384
Cross-validation details (33% Holdout set)
F measure
0.6704
Per class
Cross-validation details (33% Holdout set)
KB relative information score
-112.2808
Cross-validation details (33% Holdout set)
Mean absolute error
0.2089
Cross-validation details (33% Holdout set)
Mean prior absolute error
0.1103
Cross-validation details (33% Holdout set)
Number of instances
135
Per class
Cross-validation details (33% Holdout set)
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.5935
Per class
Cross-validation details (33% Holdout set)
Predictive accuracy
0.7704
Cross-validation details (33% Holdout set)
Prior entropy
1.1781
Cross-validation details (33% Holdout set)
Recall
0.7704
Per class
Cross-validation details (33% Holdout set)
Relative absolute error
1.8936
Cross-validation details (33% Holdout set)
Root mean prior squared error
0.2325
Cross-validation details (33% Holdout set)
Root mean squared error
0.3091
Cross-validation details (33% Holdout set)
Root relative squared error
1.3297
Cross-validation details (33% Holdout set)
SciMark benchmark
937.4673