DEVELOPMENT... OpenML
Run
157054

Run 157054

Task 3716 (Supervised Classification) tecator 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
0.9822
Per class
F measure
0.9459
Per class
Kappa
0.8893
KB relative information score
189.8922
Mean absolute error
0.111
Mean prior absolute error
0.4888
Number of instances
240
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.9459
Per class
Predictive accuracy
0.9458
Prior entropy
0.984
Recall
0.9458
Per class
Relative absolute error
0.2271
Root mean prior squared error
0.4943
Root mean squared error
0.2193
Root relative squared error
0.4437
SciMark benchmark
931.2336
UserCPU time millis
50
UserCPU time millis training
50