DEVELOPMENT... OpenML
Run
161241

Run 161241

Task 3923 (Supervised Classification) datatrieve 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
0.7739
Per class
F measure
0.8597
Per class
Kappa
0.0366
KB relative information score
-9.6107
Mean absolute error
0.1244
Mean prior absolute error
0.1601
Number of instances
130
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.8509
Per class
Predictive accuracy
0.8692
Prior entropy
0.4395
Recall
0.8692
Per class
Relative absolute error
0.7766
Root mean prior squared error
0.2784
Root mean squared error
0.3428
Root relative squared error
1.2313
SciMark benchmark
938.4285
UserCPU time millis
70
UserCPU time millis training
70

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