DEVELOPMENT... OpenML
Run
160955

Run 160955

Task 3640 (Supervised Classification) disclosure_x_bias Uploaded 15-02-2015 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
0.5367
Per class
F measure
0.5245
Per class
Kappa
0.047
KB relative information score
20.0911
Mean absolute error
0.4855
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
0.5244
Per class
Predictive accuracy
0.5257
Prior entropy
0.9987
Recall
0.5257
Per class
Relative absolute error
0.9727
Root mean prior squared error
0.4996
Root mean squared error
0.5206
Root relative squared error
1.042
SciMark benchmark
936.6206
UserCPU time millis
130
UserCPU time millis training
130