DEVELOPMENT... OpenML
Run
158203

Run 158203

Task 3840 (Supervised Classification) letter 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
0.9762
Per class
F measure
0.9825
Per class
Kappa
0.7593
KB relative information score
4565.5229
Mean absolute error
0.0341
Mean prior absolute error
0.078
Number of instances
20000
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.9831
Per class
Predictive accuracy
0.9839
Prior entropy
0.2455
Recall
0.9839
Per class
Relative absolute error
0.4367
Root mean prior squared error
0.1975
Root mean squared error
0.1185
Root relative squared error
0.6002
SciMark benchmark
932.3943
UserCPU time millis
1440
UserCPU time millis testing
20
UserCPU time millis training
1420