DEVELOPMENT... OpenML
Run
157731

Run 157731

Task 3497 (Supervised Classification) grub-damage 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)_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
0.7012
Per class
F measure
0.4515
Per class
Kappa
0.2356
KB relative information score
42.8787
Mean absolute error
0.3005
Mean prior absolute error
0.3638
Number of instances
155
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.4632
Per class
Predictive accuracy
0.4516
Prior entropy
1.9281
Recall
0.4516
Per class
Relative absolute error
0.826
Root mean prior squared error
0.4263
Root mean squared error
0.4171
Root relative squared error
0.9785
SciMark benchmark
910.8389
UserCPU time millis
10
UserCPU time millis training
10