DEVELOPMENT... OpenML
Run
159082

Run 159082

Task 3499 (Supervised Classification) squash-stored 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)_I40
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
0.7597
Per class
F measure
0.6523
Per class
Kappa
0.4338
KB relative information score
24.4681
Mean absolute error
0.2369
Mean prior absolute error
0.4135
Number of instances
52
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.653
Per class
Predictive accuracy
0.6538
Prior entropy
1.4782
Recall
0.6538
Per class
Relative absolute error
0.5728
Root mean prior squared error
0.4538
Root mean squared error
0.4527
Root relative squared error
0.9975
SciMark benchmark
943.2817
UserCPU time millis
20
UserCPU time millis training
20