DEVELOPMENT... OpenML
Run
157914

Run 157914

Task 3559 (Supervised Classification) confidence 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
0.952
Per class
F measure
0.7933
Per class
Kappa
0.75
KB relative information score
56.2388
Mean absolute error
0.0696
Mean prior absolute error
0.2778
Number of instances
72
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.7993
Per class
Predictive accuracy
0.7917
Prior entropy
2.585
Recall
0.7917
Per class
Relative absolute error
0.2506
Root mean prior squared error
0.3727
Root mean squared error
0.2443
Root relative squared error
0.6555
SciMark benchmark
902.4773
UserCPU time millis
10
UserCPU time millis training
10