DEVELOPMENT... OpenML
Run
157405

Run 157405

Task 135 (Supervised Classification) BNG(labor,nominal,1000000) 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)_I20
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
0.9853
Per class
F measure
0.9421
Per class
Kappa
0.8733
KB relative information score
798308.3432
Mean absolute error
0.0991
Mean prior absolute error
0.4576
Number of instances
1000000
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.9421
Per class
Predictive accuracy
0.9423
Prior entropy
0.938
Recall
0.9423
Per class
Relative absolute error
0.2166
Root mean prior squared error
0.4784
Root mean squared error
0.2093
Root relative squared error
0.4376
SciMark benchmark
902.4773
UserCPU time millis
22900
UserCPU time millis testing
840
UserCPU time millis training
22060

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