DEVELOPMENT... OpenML
Run
160034

Run 160034

Task 3660 (Supervised Classification) disclosure_x_tampered Uploaded 13-02-2015 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)_I80
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
0.5042
Per class
F measure
0.4843
Per class
Kappa
-0.0311
KB relative information score
4.8238
Mean absolute error
0.4961
Mean prior absolute error
0.4999
Number of instances
662
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.4845
Per class
Predictive accuracy
0.4849
Prior entropy
0.9999
Recall
0.4849
Per class
Relative absolute error
0.9923
Root mean prior squared error
0.5
Root mean squared error
0.5202
Root relative squared error
1.0405
SciMark benchmark
924.6116
UserCPU time millis
60
UserCPU time millis testing
10
UserCPU time millis training
50