DEVELOPMENT... OpenML
Run
156869

Run 156869

Task 3518 (Supervised Classification) tr45.wc 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
0.9988
Per class
F measure
0.9593
Per class
Kappa
0.9527
KB relative information score
656.0501
Mean absolute error
0.0109
Mean prior absolute error
0.172
Number of instances
690
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.9599
Per class
Predictive accuracy
0.9594
Prior entropy
3.0415
Recall
0.9594
Per class
Relative absolute error
0.0633
Root mean prior squared error
0.2932
Root mean squared error
0.0732
Root relative squared error
0.2497
SciMark benchmark
902.4773
UserCPU time millis
552980
UserCPU time millis training
552980

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