DEVELOPMENT... OpenML
Run
160215

Run 160215

Task 3868 (Supervised Classification) glass 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
0.8331
Per class
F measure
0.7916
Per class
Kappa
0.5402
KB relative information score
99.3076
Mean absolute error
0.246
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
0.7912
Per class
Predictive accuracy
0.7944
Prior entropy
0.9397
Recall
0.7944
Per class
Relative absolute error
0.5367
Root mean prior squared error
0.4786
Root mean squared error
0.4037
Root relative squared error
0.8437
SciMark benchmark
924.6116
UserCPU time millis
40
UserCPU time millis training
40