DEVELOPMENT... OpenML
Run
156618

Run 156618

Task 40 (Supervised Classification) glass Uploaded 11-02-2015 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)_I10
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
0.88
Per class
F measure
0.6995
Per class
Kappa
0.5962
KB relative information score
125.5584
Mean absolute error
0.1057
Mean prior absolute error
0.2116
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
0.7128
Per class
Predictive accuracy
0.7103
Prior entropy
2.2315
Recall
0.7103
Per class
Relative absolute error
0.4996
Root mean prior squared error
0.3244
Root mean squared error
0.2457
Root relative squared error
0.7573
SciMark benchmark
938.9865
UserCPU time millis
30
UserCPU time millis training
30