DEVELOPMENT... OpenML
Run
157436

Run 157436

Task 156 (Supervised Classification) BNG(segment) 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
0.9785
Per class
F measure
0.8565
Per class
Kappa
0.8324
KB relative information score
817427.8271
Mean absolute error
0.0625
Mean prior absolute error
0.2449
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
0.8578
Per class
Predictive accuracy
0.8563
Prior entropy
2.8074
Recall
0.8563
Per class
Relative absolute error
0.2553
Root mean prior squared error
0.3499
Root mean squared error
0.1718
Root relative squared error
0.4909
SciMark benchmark
923.3111
UserCPU time millis
174030
UserCPU time millis testing
1480
UserCPU time millis training
172550