DEVELOPMENT... OpenML
Run
176730

Run 176730

Task 2138 (Supervised Classification) BNG(letter) Uploaded 22-02-2015 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
0.9444
Per class
F measure
0.6176
Per class
Kappa
0.6042
KB relative information score
624883.9817
Mean absolute error
0.0417
Mean prior absolute error
0.074
Number of instances
1000000
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.6179
Per class
Predictive accuracy
0.6195
Prior entropy
4.6999
Recall
0.6195
Per class
Relative absolute error
0.5637
Root mean prior squared error
0.1923
Root mean squared error
0.1403
Root relative squared error
0.7294
SciMark benchmark
933.1776
UserCPU time millis
55250610
UserCPU time millis testing
4580
UserCPU time millis training
55246030