DEVELOPMENT... OpenML
Run
160806

Run 160806

Task 3510 (Supervised Classification) JapaneseVowels 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
0.9987
Per class
F measure
0.9606
Per class
Kappa
0.9554
KB relative information score
9515.8608
Mean absolute error
0.013
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
0.9606
Per class
Predictive accuracy
0.9606
Prior entropy
3.1321
Recall
0.9606
Per class
Relative absolute error
0.0665
Root mean prior squared error
0.3132
Root mean squared error
0.0817
Root relative squared error
0.2609
SciMark benchmark
943.2817
UserCPU time millis
50980
UserCPU time millis testing
20
UserCPU time millis training
50960