DEVELOPMENT... OpenML
Run
160237

Run 160237

Task 3893 (Supervised Classification) sylva_prior 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
0.9992
Per class
F measure
0.9945
Per class
Kappa
0.9529
KB relative information score
13102.4132
Mean absolute error
0.0081
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
0.9946
Per class
Predictive accuracy
0.9945
Prior entropy
0.3338
Recall
0.9945
Per class
Relative absolute error
0.0702
Root mean prior squared error
0.2403
Root mean squared error
0.0648
Root relative squared error
0.2696
SciMark benchmark
924.6116
UserCPU time millis
33790
UserCPU time millis testing
10
UserCPU time millis training
33780