DEVELOPMENT... OpenML
Run
160148

Run 160148

Task 3800 (Supervised Classification) fri_c3_500_50 Uploaded 13-02-2015 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)_I80
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
0.9299
Per class
F measure
0.8655
Per class
Kappa
0.7257
KB relative information score
324.3657
Mean absolute error
0.1806
Mean prior absolute error
0.4918
Number of instances
500
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.8661
Per class
Predictive accuracy
0.866
Prior entropy
0.9882
Recall
0.866
Per class
Relative absolute error
0.3672
Root mean prior squared error
0.4959
Root mean squared error
0.3202
Root relative squared error
0.6456
SciMark benchmark
924.6116
UserCPU time millis
410
UserCPU time millis training
410