Run
199746

Run 199746

Task 3056 (Supervised Classification) SPECT Uploaded 27-02-2015 by Joaquin Vanschoren
0 likes downloaded by 0 people 0 issues 0 downvotes , 0 total downloads
Issue #Downvotes for this reason By


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
0.7479
Per class
Cross-validation details (33% Holdout set)
F measure
0.7951
Per class
Cross-validation details (33% Holdout set)
Kappa
0.4374
Cross-validation details (33% Holdout set)
KB relative information score
22.8156
Cross-validation details (33% Holdout set)
Mean absolute error
0.2159
Cross-validation details (33% Holdout set)
Mean prior absolute error
0.3342
Cross-validation details (33% Holdout set)
Number of instances
88
Per class
Cross-validation details (33% Holdout set)
OS information
[Oracle Corporation, 1.7.0_75, amd64, Linux, 3.16.0-29-generic]
Precision
0.8162
Per class
Cross-validation details (33% Holdout set)
Predictive accuracy
0.7841
Cross-validation details (33% Holdout set)
Prior entropy
0.738
Cross-validation details (33% Holdout set)
Recall
0.7841
Per class
Cross-validation details (33% Holdout set)
Relative absolute error
0.6461
Cross-validation details (33% Holdout set)
Root mean prior squared error
0.4115
Cross-validation details (33% Holdout set)
Root mean squared error
0.4647
Cross-validation details (33% Holdout set)
Root relative squared error
1.1291
Cross-validation details (33% Holdout set)
SciMark benchmark
1404.1865