DEVELOPMENT... OpenML
Run
159479

Run 159479

Task 3865 (Supervised Classification) ipums_la_98-small Uploaded 12-02-2015 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)_I40
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
0.865
Per class
F measure
0.8591
Per class
Kappa
0.1245
KB relative information score
-1240.0261
Mean absolute error
0.1495
Mean prior absolute error
0.1891
Number of instances
7485
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.8533
Per class
Predictive accuracy
0.8915
Prior entropy
0.4871
Recall
0.8915
Per class
Relative absolute error
0.7908
Root mean prior squared error
0.3074
Root mean squared error
0.2777
Root relative squared error
0.9034
SciMark benchmark
911.0478
UserCPU time millis
1950
UserCPU time millis testing
10
UserCPU time millis training
1940