DEVELOPMENT... OpenML
Run
159636

Run 159636

Task 2073 (Supervised Classification) yeast 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)_I80
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
0.8311
Per class
F measure
0.5898
Per class
Kappa
0.4742
KB relative information score
698.4746
Mean absolute error
0.0999
Mean prior absolute error
0.1555
Number of instances
1484
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.5877
Per class
Predictive accuracy
0.5957
Prior entropy
2.5035
Recall
0.5957
Per class
Relative absolute error
0.6422
Root mean prior squared error
0.2787
Root mean squared error
0.2378
Root relative squared error
0.8533
SciMark benchmark
924.6116
UserCPU time millis
1790
UserCPU time millis training
1790

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