DEVELOPMENT... OpenML
Run
157237

Run 157237

Task 3899 (Supervised Classification) mozilla4 Uploaded 11-02-2015 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)_I10
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
0.954
Per class
F measure
0.9285
Per class
Kappa
0.835
KB relative information score
11817.1031
Mean absolute error
0.1137
Mean prior absolute error
0.4412
Number of instances
15545
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.934
Per class
Predictive accuracy
0.9304
Prior entropy
0.9135
Recall
0.9304
Per class
Relative absolute error
0.2577
Root mean prior squared error
0.4697
Root mean squared error
0.2345
Root relative squared error
0.4993
SciMark benchmark
931.2336
UserCPU time millis
220
UserCPU time millis testing
10
UserCPU time millis training
210