DEVELOPMENT... OpenML
Run
157927

Run 157927

Task 3518 (Supervised Classification) tr45.wc 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)_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
0.9994
Per class
F measure
0.9709
Per class
Kappa
0.9663
KB relative information score
663.9638
Mean absolute error
0.0068
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
0.9714
Per class
Predictive accuracy
0.971
Prior entropy
3.0415
Recall
0.971
Per class
Relative absolute error
0.0395
Root mean prior squared error
0.2932
Root mean squared error
0.0657
Root relative squared error
0.2241
SciMark benchmark
941.7954
UserCPU time millis
1093820
UserCPU time millis training
1093820