DEVELOPMENT... OpenML
Run
160015

Run 160015

Task 3640 (Supervised Classification) disclosure_x_bias Uploaded 13-02-2015 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)_I80
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
0.5426
Per class
F measure
0.519
Per class
Kappa
0.0358
KB relative information score
19.3845
Mean absolute error
0.4863
Mean prior absolute error
0.4991
Number of instances
662
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.5188
Per class
Predictive accuracy
0.5196
Prior entropy
0.9987
Recall
0.5196
Per class
Relative absolute error
0.9742
Root mean prior squared error
0.4996
Root mean squared error
0.5139
Root relative squared error
1.0287
SciMark benchmark
924.6116
UserCPU time millis
50
UserCPU time millis training
50