DEVELOPMENT... OpenML
Run
161209

Run 161209

Task 3889 (Supervised Classification) sylva_agnostic Uploaded 15-02-2015 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)_I160
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
0.9991
Per class
F measure
0.9941
Per class
Kappa
0.9497
KB relative information score
13144.9094
Mean absolute error
0.0077
Mean prior absolute error
0.1156
Number of instances
14395
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.9942
Per class
Predictive accuracy
0.9941
Prior entropy
0.3338
Recall
0.9941
Per class
Relative absolute error
0.0667
Root mean prior squared error
0.2403
Root mean squared error
0.0678
Root relative squared error
0.2823
SciMark benchmark
936.6206
UserCPU time millis
98410
UserCPU time millis testing
30
UserCPU time millis training
98380