DEVELOPMENT... OpenML
Run
160985

Run 160985

Task 3670 (Supervised Classification) fri_c4_500_50 Uploaded 15-02-2015 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)_I160
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
0.9367
Per class
F measure
0.864
Per class
Kappa
0.7273
KB relative information score
344.3647
Mean absolute error
0.1587
Mean prior absolute error
0.4984
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
0.8641
Per class
Predictive accuracy
0.864
Prior entropy
0.9978
Recall
0.864
Per class
Relative absolute error
0.3183
Root mean prior squared error
0.4992
Root mean squared error
0.326
Root relative squared error
0.6531
SciMark benchmark
936.6206
UserCPU time millis
950
UserCPU time millis training
950

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