DEVELOPMENT... OpenML
Run
157474

Run 157474

Task 2075 (Supervised Classification) abalone 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
0.7571
Per class
F measure
0.2389
Per class
Kappa
0.1598
KB relative information score
1008.0032
Mean absolute error
0.0559
Mean prior absolute error
0.0618
Number of instances
4177
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.2345
Per class
Predictive accuracy
0.2629
Prior entropy
3.6268
Recall
0.2629
Per class
Relative absolute error
0.9054
Root mean prior squared error
0.1757
Root mean squared error
0.1685
Root relative squared error
0.9588
SciMark benchmark
932.3943
UserCPU time millis
4190
UserCPU time millis testing
20
UserCPU time millis training
4170