DEVELOPMENT... OpenML
Run
158233

Run 158233

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)_I20
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
0.866
Per class
F measure
0.8589
Per class
Kappa
0.1156
KB relative information score
-1367.4462
Mean absolute error
0.1509
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
0.8582
Per class
Predictive accuracy
0.8941
Prior entropy
0.4871
Recall
0.8941
Per class
Relative absolute error
0.7977
Root mean prior squared error
0.3074
Root mean squared error
0.2771
Root relative squared error
0.9013
SciMark benchmark
932.3943
UserCPU time millis
860
UserCPU time millis training
860