DEVELOPMENT... OpenML
Run
159491

Run 159491

Task 3889 (Supervised Classification) sylva_agnostic 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
0.999
Per class
F measure
0.9939
Per class
Kappa
0.9478
KB relative information score
12795.6406
Mean absolute error
0.0101
Mean prior absolute error
0.1156
Number of instances
14395
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.994
Per class
Predictive accuracy
0.9939
Prior entropy
0.3338
Recall
0.9939
Per class
Relative absolute error
0.0872
Root mean prior squared error
0.2403
Root mean squared error
0.0689
Root relative squared error
0.2868
SciMark benchmark
945.6434
UserCPU time millis
32050
UserCPU time millis testing
20
UserCPU time millis training
32030

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