Run
199740

Run 199740

Task 3055 (Supervised Classification) monks-problems-3 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
0.967
Per class
Cross-validation details (33% Holdout set)
F measure
0.9617
Per class
Cross-validation details (33% Holdout set)
Kappa
0.9219
Cross-validation details (33% Holdout set)
KB relative information score
166.6647
Cross-validation details (33% Holdout set)
Mean absolute error
0.0385
Cross-validation details (33% Holdout set)
Mean prior absolute error
0.4967
Cross-validation details (33% Holdout set)
Number of instances
182
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
0.9648
Per class
Cross-validation details (33% Holdout set)
Predictive accuracy
0.9615
Cross-validation details (33% Holdout set)
Prior entropy
0.9989
Cross-validation details (33% Holdout set)
Recall
0.9615
Per class
Cross-validation details (33% Holdout set)
Relative absolute error
0.0774
Cross-validation details (33% Holdout set)
Root mean prior squared error
0.4971
Cross-validation details (33% Holdout set)
Root mean squared error
0.1961
Cross-validation details (33% Holdout set)
Root relative squared error
0.3945
Cross-validation details (33% Holdout set)
SciMark benchmark
869.6028