DEVELOPMENT... OpenML
Run
158281

Run 158281

Task 3914 (Supervised Classification) ar6 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)_I20
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
0.6252
Per class
F measure
0.8094
Per class
Kappa
0.1757
KB relative information score
5.9681
Mean absolute error
0.2059
Mean prior absolute error
0.2577
Number of instances
101
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.7973
Per class
Predictive accuracy
0.8317
Prior entropy
0.623
Recall
0.8317
Per class
Relative absolute error
0.7989
Root mean prior squared error
0.3557
Root mean squared error
0.3758
Root relative squared error
1.0567
SciMark benchmark
932.3943
UserCPU time millis
10
UserCPU time millis training
10