DEVELOPMENT... OpenML
Run
161142

Run 161142

Task 3827 (Supervised Classification) pasture 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
0.9219
Per class
F measure
0.8553
Per class
Kappa
0.6667
KB relative information score
24.0652
Mean absolute error
0.139
Mean prior absolute error
0.4474
Number of instances
36
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.8642
Per class
Predictive accuracy
0.8611
Prior entropy
0.9268
Recall
0.8611
Per class
Relative absolute error
0.3107
Root mean prior squared error
0.4715
Root mean squared error
0.3723
Root relative squared error
0.7897
SciMark benchmark
938.4285
UserCPU time millis
30
UserCPU time millis training
30