DEVELOPMENT... OpenML
Run
159408

Run 159408

Task 3800 (Supervised Classification) fri_c3_500_50 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
0.9363
Per class
F measure
0.8755
Per class
Kappa
0.7463
KB relative information score
310.3883
Mean absolute error
0.1982
Mean prior absolute error
0.4918
Number of instances
500
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.8761
Per class
Predictive accuracy
0.876
Prior entropy
0.9882
Recall
0.876
Per class
Relative absolute error
0.4029
Root mean prior squared error
0.4959
Root mean squared error
0.3119
Root relative squared error
0.629
SciMark benchmark
924.6116
UserCPU time millis
200
UserCPU time millis training
200