DEVELOPMENT... OpenML
Run
159433

Run 159433

Task 3827 (Supervised Classification) pasture 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
0.8924
Per class
F measure
0.7395
Per class
Kappa
0.4
KB relative information score
16.657
Mean absolute error
0.2243
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
0.7407
Per class
Predictive accuracy
0.75
Prior entropy
0.9268
Recall
0.75
Per class
Relative absolute error
0.5013
Root mean prior squared error
0.4715
Root mean squared error
0.4534
Root relative squared error
0.9617
SciMark benchmark
924.6116
UserCPU time millis
10
UserCPU time millis training
10