DEVELOPMENT... OpenML
Run
160790

Run 160790

Task 3497 (Supervised Classification) grub-damage Uploaded 14-02-2015 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)_I160
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
0.6829
Per class
F measure
0.4659
Per class
Kappa
0.2625
KB relative information score
44.5726
Mean absolute error
0.2921
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
0.4688
Per class
Predictive accuracy
0.4645
Prior entropy
1.9281
Recall
0.4645
Per class
Relative absolute error
0.8029
Root mean prior squared error
0.4263
Root mean squared error
0.4561
Root relative squared error
1.0699
SciMark benchmark
945.7621
UserCPU time millis
80
UserCPU time millis training
80