DEVELOPMENT... OpenML
Run
158776

Run 158776

Task 3535 (Supervised Classification) tr41.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
0.9996
Per class
F measure
0.9754
Per class
Kappa
0.971
KB relative information score
848.7944
Mean absolute error
0.0052
Mean prior absolute error
0.1652
Number of instances
878
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.9763
Per class
Predictive accuracy
0.9761
Prior entropy
2.8041
Recall
0.9761
Per class
Relative absolute error
0.0316
Root mean prior squared error
0.2872
Root mean squared error
0.0572
Root relative squared error
0.1993
SciMark benchmark
910.8389
UserCPU time millis
1152900
UserCPU time millis training
1152900