DEVELOPMENT... OpenML
Run
156757

Run 156757

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)_I10
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
0.5971
Per class
Kappa
0.4867
KB relative information score
693.7584
Mean absolute error
0.1038
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
0.595
Per class
Predictive accuracy
0.6058
Prior entropy
2.5035
Recall
0.6058
Per class
Relative absolute error
0.6671
Root mean prior squared error
0.2787
Root mean squared error
0.2319
Root relative squared error
0.832
SciMark benchmark
938.9865
UserCPU time millis
220
UserCPU time millis training
220