DEVELOPMENT... OpenML
Run
161109

Run 161109

Task 3794 (Supervised Classification) disclosure_z Uploaded 15-02-2015 by Felicia West
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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)_I160
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.

22 Evaluation measures

Area under ROC curve
0.5223
Per class
F measure
0.5396
Per class
Kappa
0.0763
KB relative information score
11.3505
Mean absolute error
0.4908
Mean prior absolute error
0.4987
Number of instances
662
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.5395
Per class
Predictive accuracy
0.5408
Prior entropy
0.9981
Recall
0.5408
Per class
Relative absolute error
0.9842
Root mean prior squared error
0.4993
Root mean squared error
0.5276
Root relative squared error
1.0565
SciMark benchmark
936.6206
UserCPU time millis
100
UserCPU time millis training
100