DEVELOPMENT... OpenML
Run
159343

Run 159343

Task 3724 (Supervised Classification) vinnie 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
0.9208
Per class
F measure
0.8394
Per class
Kappa
0.6785
KB relative information score
223.9203
Mean absolute error
0.2152
Mean prior absolute error
0.4997
Number of instances
380
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.8396
Per class
Predictive accuracy
0.8395
Prior entropy
0.9995
Recall
0.8395
Per class
Relative absolute error
0.4307
Root mean prior squared error
0.4998
Root mean squared error
0.334
Root relative squared error
0.6683
SciMark benchmark
924.6116
UserCPU time millis
10
UserCPU time millis training
10