DEVELOPMENT... OpenML
Run
157220

Run 157220

Task 3882 (Supervised Classification) pendigits 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
0.993
Per class
F measure
0.9685
Per class
Kappa
0.8307
KB relative information score
7774.0133
Mean absolute error
0.0508
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
0.9684
Per class
Predictive accuracy
0.9685
Prior entropy
0.482
Recall
0.9685
Per class
Relative absolute error
0.2722
Root mean prior squared error
0.3054
Root mean squared error
0.142
Root relative squared error
0.4651
SciMark benchmark
931.2336
UserCPU time millis
410
UserCPU time millis testing
10
UserCPU time millis training
400