DEVELOPMENT... OpenML
Run
157597

Run 157597

Task 2138 (Supervised Classification) BNG(letter) Uploaded 12-02-2015 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
0.9185
Per class
F measure
0.541
Per class
Kappa
0.5277
KB relative information score
524485.0844
Mean absolute error
0.0513
Mean prior absolute error
0.074
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
0.5435
Per class
Predictive accuracy
0.546
Prior entropy
4.6999
Recall
0.546
Per class
Relative absolute error
0.6942
Root mean prior squared error
0.1923
Root mean squared error
0.1539
Root relative squared error
0.8003
SciMark benchmark
943.2745
UserCPU time millis
3862090
UserCPU time millis testing
4710
UserCPU time millis training
3857380