DEVELOPMENT... OpenML
Run
161174

Run 161174

Task 3859 (Supervised Classification) prnn_fglass 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
0.8145
Per class
F measure
0.7981
Per class
Kappa
0.5574
KB relative information score
104.3287
Mean absolute error
0.2332
Mean prior absolute error
0.4584
Number of instances
214
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.7975
Per class
Predictive accuracy
0.7991
Prior entropy
0.9397
Recall
0.7991
Per class
Relative absolute error
0.5086
Root mean prior squared error
0.4786
Root mean squared error
0.4147
Root relative squared error
0.8665
SciMark benchmark
936.6206
UserCPU time millis
60
UserCPU time millis training
60