DEVELOPMENT... OpenML
Run
156720

Run 156720

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)_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
0.977
Per class
F measure
0.8429
Per class
Kappa
0.8167
KB relative information score
802187.1565
Mean absolute error
0.0677
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
0.8449
Per class
Predictive accuracy
0.8429
Prior entropy
2.8074
Recall
0.8429
Per class
Relative absolute error
0.2766
Root mean prior squared error
0.3499
Root mean squared error
0.1767
Root relative squared error
0.5049
SciMark benchmark
943.2817
UserCPU time millis
86880
UserCPU time millis testing
2100
UserCPU time millis training
84780