Run
53537

Run 53537

Task 1714 (Learning Curve) breast-w Uploaded 08-11-2014 by Jan van Rijn
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  • curves study_2 study_37 weka weka_3.7.12-SNAPSHOT
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Flow

weka.LogitBoost_DecisionStump(2)J. Friedman, T. Hastie, R. Tibshirani (1998). Additive Logistic Regression: a Statistical View of Boosting. Stanford University.
weka.LogitBoost_DecisionStump(2)_E1
weka.LogitBoost_DecisionStump(2)_H1.0
weka.LogitBoost_DecisionStump(2)_I10
weka.LogitBoost_DecisionStump(2)_L-1.7976931348623157E308
weka.LogitBoost_DecisionStump(2)_O1
weka.LogitBoost_DecisionStump(2)_P100
weka.LogitBoost_DecisionStump(2)_S1
weka.LogitBoost_DecisionStump(2)_Wweka.classifiers.trees.DecisionStump
weka.LogitBoost_DecisionStump(2)_Z3.0

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
0.9911 ± 0.007
Per class
Build CPU time
0.0044
Build memory
2230832653
F measure
0.9542 ± 0.0274
Per class
Kappa
0.8985 ± 0.0604
KB relative information score
0.8755 ± 0.042
Mean absolute error
0.0574 ± 0.0166
Mean prior absolute error
0.4519 ± 0.0014
Number of instances
699
Per class
OS information
[Sun Microsystems Inc., 1.6.0_33, amd64, Linux, 3.5.0-54-generic]
Precision
0.9541 ± 0.0263
Per class
Predictive accuracy
0.9542 ± 0.0276
Prior entropy
0.9293 ± 0.0043
Recall
0.9542 ± 0.0276
Per class
Relative absolute error
0.1269 ± 0.0367
Root mean prior squared error
0.4753 ± 0.0015
Root mean squared error
0.1862 ± 0.0513
Root relative squared error
0.3919 ± 0.1078
SciMark benchmark
1597.1006