DEVELOPMENT... OpenML
Run
161112

Run 161112

Task 3797 (Supervised Classification) socmob 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
0.987
Per class
F measure
0.9528
Per class
Kappa
0.8618
KB relative information score
933.5416
Mean absolute error
0.0609
Mean prior absolute error
0.3451
Number of instances
1156
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.9527
Per class
Predictive accuracy
0.9533
Prior entropy
0.7637
Recall
0.9533
Per class
Relative absolute error
0.1765
Root mean prior squared error
0.4152
Root mean squared error
0.1913
Root relative squared error
0.4607
SciMark benchmark
936.6206
UserCPU time millis
110
UserCPU time millis training
110