DEVELOPMENT... OpenML
Run
161204

Run 161204

Task 3881 (Supervised Classification) ipums_la_99-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
0.8954
Per class
F measure
0.9197
Per class
Kappa
0.2145
KB relative information score
-2626.687
Mean absolute error
0.0933
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
0.9162
Per class
Predictive accuracy
0.9358
Prior entropy
0.3444
Recall
0.9358
Per class
Relative absolute error
0.7754
Root mean prior squared error
0.2452
Root mean squared error
0.2223
Root relative squared error
0.9067
SciMark benchmark
933.8635
UserCPU time millis
10120
UserCPU time millis testing
10
UserCPU time millis training
10110