DEVELOPMENT... OpenML
Run
159483

Run 159483

Task 3881 (Supervised Classification) ipums_la_99-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
0.897
Per class
F measure
0.9159
Per class
Kappa
0.1608
KB relative information score
-2848.376
Mean absolute error
0.0947
Mean prior absolute error
0.1203
Number of instances
8844
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.9129
Per class
Predictive accuracy
0.9354
Prior entropy
0.3444
Recall
0.9354
Per class
Relative absolute error
0.7873
Root mean prior squared error
0.2452
Root mean squared error
0.2211
Root relative squared error
0.9019
SciMark benchmark
945.6434
UserCPU time millis
2210
UserCPU time millis training
2210