DEVELOPMENT... OpenML
Run
160250

Run 160250

Task 3903 (Supervised Classification) pc3 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
0.8147
Per class
F measure
0.863
Per class
Kappa
0.1576
KB relative information score
-164.4799
Mean absolute error
0.149
Mean prior absolute error
0.1842
Number of instances
1563
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.8515
Per class
Predictive accuracy
0.8842
Prior entropy
0.478
Recall
0.8842
Per class
Relative absolute error
0.809
Root mean prior squared error
0.3031
Root mean squared error
0.294
Root relative squared error
0.97
SciMark benchmark
943.2817
UserCPU time millis
810
UserCPU time millis training
810