DEVELOPMENT... OpenML
Run
159345

Run 159345

Task 3726 (Supervised Classification) sleuth_ex2016 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
0.7646
Per class
F measure
0.6438
Per class
Kappa
0.2871
KB relative information score
28.774
Mean absolute error
0.3341
Mean prior absolute error
0.4994
Number of instances
87
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.644
Per class
Predictive accuracy
0.6437
Prior entropy
0.9992
Recall
0.6437
Per class
Relative absolute error
0.669
Root mean prior squared error
0.4997
Root mean squared error
0.4994
Root relative squared error
0.9993
SciMark benchmark
924.6116
UserCPU time millis
10
UserCPU time millis training
10