DEVELOPMENT... OpenML
Run
161190

Run 161190

Task 3856 (Supervised Classification) kdd_ipums_la_97-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
0.9935
Per class
F measure
0.9753
Per class
Kappa
0.9471
KB relative information score
6522.1865
Mean absolute error
0.0346
Mean prior absolute error
0.466
Number of instances
7019
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.9756
Per class
Predictive accuracy
0.9752
Prior entropy
0.9504
Recall
0.9752
Per class
Relative absolute error
0.0742
Root mean prior squared error
0.4827
Root mean squared error
0.1426
Root relative squared error
0.2954
SciMark benchmark
938.4285
UserCPU time millis
9520
UserCPU time millis training
9520