DEVELOPMENT... OpenML
Run
161196

Run 161196

Task 3865 (Supervised Classification) ipums_la_98-small 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
0.8603
Per class
F measure
0.8657
Per class
Kappa
0.1802
KB relative information score
-1143.2749
Mean absolute error
0.1489
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
0.8593
Per class
Predictive accuracy
0.8915
Prior entropy
0.4871
Recall
0.8915
Per class
Relative absolute error
0.7873
Root mean prior squared error
0.3074
Root mean squared error
0.2814
Root relative squared error
0.9154
SciMark benchmark
936.6206
UserCPU time millis
6620
UserCPU time millis training
6620