Run
199738

Run 199738

Task 3053 (Supervised Classification) monks-problems-1 Uploaded 27-02-2015 by Joaquin Vanschoren
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Flow

weka.Winnow(3)N. Littlestone (1988). Learning quickly when irrelevant attributes are abound: A new linear threshold algorithm. Machine Learning. 2:285-318. N. Littlestone (1989). Mistake bounds and logarithmic linear-threshold learning algorithms. University of California, Santa Cruz.
weka.Winnow(3)_I1
weka.Winnow(3)_A2.0
weka.Winnow(3)_B0.5
weka.Winnow(3)_H-1.0
weka.Winnow(3)_W2.0
weka.Winnow(3)_S1

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
0.7056
Per class
Cross-validation details (33% Holdout set)
F measure
0.7049
Per class
Cross-validation details (33% Holdout set)
Kappa
0.4105
Cross-validation details (33% Holdout set)
KB relative information score
75
Cross-validation details (33% Holdout set)
Mean absolute error
0.2951
Cross-validation details (33% Holdout set)
Mean prior absolute error
0.5
Cross-validation details (33% Holdout set)
Number of instances
183
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
0.7063
Per class
Cross-validation details (33% Holdout set)
Predictive accuracy
0.7049
Cross-validation details (33% Holdout set)
Prior entropy
1
Cross-validation details (33% Holdout set)
Recall
0.7049
Per class
Cross-validation details (33% Holdout set)
Relative absolute error
0.5902
Cross-validation details (33% Holdout set)
Root mean prior squared error
0.5
Cross-validation details (33% Holdout set)
Root mean squared error
0.5432
Cross-validation details (33% Holdout set)
Root relative squared error
1.0864
Cross-validation details (33% Holdout set)
SciMark benchmark
940.3347