DEVELOPMENT... OpenML
Run
159482

Run 159482

Task 3882 (Supervised Classification) pendigits 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)_I40
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
0.9989
Per class
F measure
0.992
Per class
Kappa
0.9573
KB relative information score
9778.5322
Mean absolute error
0.0174
Mean prior absolute error
0.1865
Number of instances
10992
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.992
Per class
Predictive accuracy
0.992
Prior entropy
0.482
Recall
0.992
Per class
Relative absolute error
0.093
Root mean prior squared error
0.3054
Root mean squared error
0.083
Root relative squared error
0.2719
SciMark benchmark
911.0478
UserCPU time millis
1810
UserCPU time millis testing
10
UserCPU time millis training
1800