Run
244299

Run 244299

Task 3908 (Supervised Classification) usp05-ft Uploaded 03-03-2015 by Joaquin Vanschoren
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Flow

weka.Logistic(5)le Cessie, S., van Houwelingen, J.C. (1992). Ridge Estimators in Logistic Regression. Applied Statistics. 41(1):191-201.
weka.Logistic(5)_R1.0E-8
weka.Logistic(5)_M-1

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
0.9889
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.9308
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.8528
Cross-validation details (10-fold Crossvalidation)
KB relative information score
51.2645
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.0169
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.1439
Cross-validation details (10-fold Crossvalidation)
Number of instances
72
Per class
Cross-validation details (10-fold Crossvalidation)
OS information
[Oracle Corporation, 1.7.0_75, amd64, Linux, 3.16.0-29-generic]
Precision
0.9176
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.9444
Cross-validation details (10-fold Crossvalidation)
Prior entropy
1.4392
Cross-validation details (10-fold Crossvalidation)
Recall
0.9444
Per class
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.1175
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.2584
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.1187
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.4593
Cross-validation details (10-fold Crossvalidation)
SciMark benchmark
1328.4045
Total cost
UserCPU time millis
80
UserCPU time millis training
80