DEVELOPMENT... OpenML
Run
161240

Run 161240

Task 3922 (Supervised Classification) desharnais Uploaded 15-02-2015 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)_I160
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
0.8437
Per class
F measure
0.7868
Per class
Kappa
0.6127
KB relative information score
49.1891
Mean absolute error
0.1496
Mean prior absolute error
0.3804
Number of instances
81
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.7986
Per class
Predictive accuracy
0.7901
Prior entropy
1.3765
Recall
0.7901
Per class
Relative absolute error
0.3933
Root mean prior squared error
0.4348
Root mean squared error
0.373
Root relative squared error
0.8578
SciMark benchmark
938.4285
UserCPU time millis
90
UserCPU time millis training
90