DEVELOPMENT... OpenML
Run
159512

Run 159512

Task 3915 (Supervised Classification) kc3 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)_I40
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
0.7472
Per class
F measure
0.8701
Per class
Kappa
0.1836
KB relative information score
-83.7118
Mean absolute error
0.1449
Mean prior absolute error
0.1716
Number of instances
458
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.8623
Per class
Predictive accuracy
0.8799
Prior entropy
0.4551
Recall
0.8799
Per class
Relative absolute error
0.8445
Root mean prior squared error
0.2917
Root mean squared error
0.3043
Root relative squared error
1.0433
SciMark benchmark
911.0478
UserCPU time millis
110
UserCPU time millis training
110