DEVELOPMENT... OpenML
Run
158266

Run 158266

Task 3899 (Supervised Classification) mozilla4 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)_I20
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
0.959
Per class
F measure
0.9343
Per class
Kappa
0.8484
KB relative information score
12091.9435
Mean absolute error
0.1053
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
0.94
Per class
Predictive accuracy
0.9361
Prior entropy
0.9135
Recall
0.9361
Per class
Relative absolute error
0.2387
Root mean prior squared error
0.4697
Root mean squared error
0.2268
Root relative squared error
0.483
SciMark benchmark
932.3943
UserCPU time millis
440
UserCPU time millis testing
20
UserCPU time millis training
420