DEVELOPMENT... OpenML
Run
159516

Run 159516

Task 3895 (Supervised Classification) gina_prior 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)_I40
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
0.9316
Per class
F measure
0.8656
Per class
Kappa
0.7312
KB relative information score
2202.3303
Mean absolute error
0.1934
Mean prior absolute error
0.4999
Number of instances
3468
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.8657
Per class
Predictive accuracy
0.8656
Prior entropy
0.9998
Recall
0.8656
Per class
Relative absolute error
0.3869
Root mean prior squared error
0.4999
Root mean squared error
0.3188
Root relative squared error
0.6376
SciMark benchmark
945.6434
UserCPU time millis
24340
UserCPU time millis testing
10
UserCPU time millis training
24330