DEVELOPMENT... OpenML
Run
157472

Run 157472

Task 2073 (Supervised Classification) yeast Uploaded 11-02-2015 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
0.837
Per class
F measure
0.5955
Per class
Kappa
0.4832
KB relative information score
701.3881
Mean absolute error
0.1017
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
0.5932
Per class
Predictive accuracy
0.6031
Prior entropy
2.5035
Recall
0.6031
Per class
Relative absolute error
0.6538
Root mean prior squared error
0.2787
Root mean squared error
0.2328
Root relative squared error
0.8351
SciMark benchmark
932.3943
UserCPU time millis
450
UserCPU time millis training
450