DEVELOPMENT... OpenML
Run
159951

Run 159951

Task 3564 (Supervised Classification) analcatdata_germangss Uploaded 13-02-2015 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)_I80
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
0.7392
Per class
F measure
0.4121
Per class
Kappa
0.22
KB relative information score
113.2976
Mean absolute error
0.3056
Mean prior absolute error
0.375
Number of instances
400
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.4104
Per class
Predictive accuracy
0.415
Prior entropy
2
Recall
0.415
Per class
Relative absolute error
0.8149
Root mean prior squared error
0.433
Root mean squared error
0.4061
Root relative squared error
0.9378
SciMark benchmark
938.2848
UserCPU time millis
70
UserCPU time millis training
70