DEVELOPMENT... OpenML
Run
157132

Run 157132

Task 3794 (Supervised Classification) disclosure_z Uploaded 11-02-2015 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)_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.

22 Evaluation measures

Area under ROC curve
0.5461
Per class
F measure
0.5192
Per class
Kappa
0.0352
KB relative information score
17.0016
Mean absolute error
0.4878
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
0.5189
Per class
Predictive accuracy
0.5196
Prior entropy
0.9981
Recall
0.5196
Per class
Relative absolute error
0.9781
Root mean prior squared error
0.4993
Root mean squared error
0.5055
Root relative squared error
1.0123
SciMark benchmark
931.2336
UserCPU time millis
10
UserCPU time millis training
10