DEVELOPMENT... OpenML
Run
160081

Run 160081

Task 3719 (Supervised Classification) fishcatch 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
0.9881
Per class
F measure
0.9559
Per class
Kappa
0.9083
KB relative information score
143.4561
Mean absolute error
0.0434
Mean prior absolute error
0.4797
Number of instances
158
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.9567
Per class
Predictive accuracy
0.9557
Prior entropy
0.971
Recall
0.9557
Per class
Relative absolute error
0.0904
Root mean prior squared error
0.4896
Root mean squared error
0.2001
Root relative squared error
0.4087
SciMark benchmark
924.6116
UserCPU time millis
20
UserCPU time millis training
20