DEVELOPMENT... OpenML
Run
157754

Run 157754

Task 3510 (Supervised Classification) JapaneseVowels Uploaded 12-02-2015 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)_I20
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
0.9935
Per class
F measure
0.9027
Per class
Kappa
0.8903
KB relative information score
8610.0897
Mean absolute error
0.0423
Mean prior absolute error
0.1962
Number of instances
9961
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.9024
Per class
Predictive accuracy
0.9032
Prior entropy
3.1321
Recall
0.9032
Per class
Relative absolute error
0.2154
Root mean prior squared error
0.3132
Root mean squared error
0.1289
Root relative squared error
0.4116
SciMark benchmark
941.7954
UserCPU time millis
8060
UserCPU time millis testing
10
UserCPU time millis training
8050