DEVELOPMENT... OpenML
Run
161203

Run 161203

Task 3888 (Supervised Classification) grub-damage Uploaded 15-02-2015 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
0.7463
Per class
F measure
0.7233
Per class
Kappa
0.3619
KB relative information score
44.8367
Mean absolute error
0.2961
Mean prior absolute error
0.4332
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
0.7241
Per class
Predictive accuracy
0.7226
Prior entropy
0.9027
Recall
0.7226
Per class
Relative absolute error
0.6834
Root mean prior squared error
0.465
Root mean squared error
0.4705
Root relative squared error
1.0119
SciMark benchmark
936.6206
UserCPU time millis
20
UserCPU time millis training
20