DEVELOPMENT... OpenML
Run
160207

Run 160207

Task 3859 (Supervised Classification) prnn_fglass 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
0.805
Per class
F measure
0.7577
Per class
Kappa
0.4726
KB relative information score
88.8805
Mean absolute error
0.2667
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
0.7585
Per class
Predictive accuracy
0.757
Prior entropy
0.9397
Recall
0.757
Per class
Relative absolute error
0.5817
Root mean prior squared error
0.4786
Root mean squared error
0.4214
Root relative squared error
0.8806
SciMark benchmark
924.6116
UserCPU time millis
30
UserCPU time millis training
30