DEVELOPMENT... OpenML
Run
159487

Run 159487

Task 3888 (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)_I40
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
0.7621
Per class
F measure
0.7314
Per class
Kappa
0.371
KB relative information score
43.2346
Mean absolute error
0.3051
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
0.729
Per class
Predictive accuracy
0.7355
Prior entropy
0.9027
Recall
0.7355
Per class
Relative absolute error
0.7042
Root mean prior squared error
0.465
Root mean squared error
0.4418
Root relative squared error
0.9502
SciMark benchmark
924.6116
UserCPU time millis
10
UserCPU time millis training
10