DEVELOPMENT... OpenML
Run
156622

Run 156622

Task 32 (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
0.994
Per class
F measure
0.9233
Per class
Kappa
0.9149
KB relative information score
9738.3352
Mean absolute error
0.0347
Mean prior absolute error
0.18
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
0.9235
Per class
Predictive accuracy
0.9234
Prior entropy
3.3208
Recall
0.9234
Per class
Relative absolute error
0.1926
Root mean prior squared error
0.3
Root mean squared error
0.1137
Root relative squared error
0.3791
SciMark benchmark
943.2817
UserCPU time millis
3680
UserCPU time millis testing
20
UserCPU time millis training
3660