DEVELOPMENT... OpenML
Run
160754

Run 160754

Task 2272 (Supervised Classification) meta_all.arff Uploaded 14-02-2015 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
0.6545
Per class
F measure
0.5926
Per class
Kappa
0.2889
KB relative information score
24.502
Mean absolute error
0.1256
Mean prior absolute error
0.2065
Number of instances
71
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.5913
Per class
Predictive accuracy
0.6197
Prior entropy
1.9094
Recall
0.6197
Per class
Relative absolute error
0.6082
Root mean prior squared error
0.317
Root mean squared error
0.3529
Root relative squared error
1.1134
SciMark benchmark
932.3943
UserCPU time millis
680
UserCPU time millis training
680