DEVELOPMENT... OpenML
Run
160183

Run 160183

Task 3835 (Supervised Classification) squash-stored Uploaded 13-02-2015 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)_I80
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
0.7751
Per class
F measure
0.7505
Per class
Kappa
0.4955
KB relative information score
24.5993
Mean absolute error
0.2594
Mean prior absolute error
0.4936
Number of instances
52
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.7515
Per class
Predictive accuracy
0.75
Prior entropy
0.9911
Recall
0.75
Per class
Relative absolute error
0.5256
Root mean prior squared error
0.4967
Root mean squared error
0.4911
Root relative squared error
0.9889
SciMark benchmark
924.6116
UserCPU time millis
20
UserCPU time millis training
20