DEVELOPMENT... OpenML
Run
159042

Run 159042

Task 2079 (Supervised Classification) eucalyptus 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
0.9022
Per class
F measure
0.6319
Per class
Kappa
0.5286
KB relative information score
420.4679
Mean absolute error
0.1626
Mean prior absolute error
0.3132
Number of instances
736
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.6333
Per class
Predictive accuracy
0.6332
Prior entropy
2.2629
Recall
0.6332
Per class
Relative absolute error
0.5192
Root mean prior squared error
0.3957
Root mean squared error
0.3072
Root relative squared error
0.7764
SciMark benchmark
924.6116
UserCPU time millis
280
UserCPU time millis training
280