DEVELOPMENT... OpenML
Run
158160

Run 158160

Task 3797 (Supervised Classification) socmob 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)_I20
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
0.9822
Per class
F measure
0.9355
Per class
Kappa
0.8117
KB relative information score
856.6193
Mean absolute error
0.0863
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
0.9353
Per class
Predictive accuracy
0.936
Prior entropy
0.7637
Recall
0.936
Per class
Relative absolute error
0.2501
Root mean prior squared error
0.4152
Root mean squared error
0.2113
Root relative squared error
0.5089
SciMark benchmark
932.3943
UserCPU time millis
20
UserCPU time millis training
20