DEVELOPMENT... OpenML
Run
157233

Run 157233

Task 3892 (Supervised Classification) hiva_agnostic 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
0.7408
Per class
F measure
0.9536
Per class
Kappa
0.1536
KB relative information score
-2060.47
Mean absolute error
0.059
Mean prior absolute error
0.0682
Number of instances
4229
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.9543
Per class
Predictive accuracy
0.9657
Prior entropy
0.221
Recall
0.9657
Per class
Relative absolute error
0.8659
Root mean prior squared error
0.1844
Root mean squared error
0.1739
Root relative squared error
0.9433
SciMark benchmark
931.2336
UserCPU time millis
15710
UserCPU time millis testing
20
UserCPU time millis training
15690