DEVELOPMENT... OpenML
Run
158255

Run 158255

Task 3891 (Supervised Classification) gina_agnostic 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
0.9153
Per class
F measure
0.8445
Per class
Kappa
0.689
KB relative information score
2018.3531
Mean absolute error
0.2218
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
0.8446
Per class
Predictive accuracy
0.8446
Prior entropy
0.9998
Recall
0.8446
Per class
Relative absolute error
0.4437
Root mean prior squared error
0.4999
Root mean squared error
0.3371
Root relative squared error
0.6743
SciMark benchmark
902.4773
UserCPU time millis
16550
UserCPU time millis testing
10
UserCPU time millis training
16540