Run
53535

Run 53535

Task 1710 (Learning Curve) balance-scale 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.

19 Evaluation measures

Area under ROC curve
0.961 ± 0.0112
Per class
Build CPU time
0.0074
Build memory
1637254688.3
Kappa
0.7656 ± 0.0511
KB relative information score
0.573 ± 0.0441
Mean absolute error
0.1626 ± 0.0193
Mean prior absolute error
0.3798 ± 0.0012
Number of instances
625
Per class
OS information
[Sun Microsystems Inc., 1.6.0_33, amd64, Linux, 3.5.0-54-generic]
Predictive accuracy
0.8736 ± 0.027
Prior entropy
1.3181 ± 0.0124
Recall
0.8736 ± 0.027
Per class
Relative absolute error
0.4281 ± 0.0508
Root mean prior squared error
0.4356 ± 0.0014
Root mean squared error
0.2598 ± 0.0144
Root relative squared error
0.5964 ± 0.0334
SciMark benchmark
1206.3605