DEVELOPMENT... OpenML
Run
156988

Run 156988

Task 3650 (Supervised Classification) wind_correlations Uploaded 11-02-2015 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)_I10
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
0.9368
Per class
F measure
0.9333
Per class
Kappa
0.8665
KB relative information score
37.1445
Mean absolute error
0.0918
Mean prior absolute error
0.4998
Number of instances
45
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.9341
Per class
Predictive accuracy
0.9333
Prior entropy
0.9997
Recall
0.9333
Per class
Relative absolute error
0.1837
Root mean prior squared error
0.4999
Root mean squared error
0.267
Root relative squared error
0.5341
SciMark benchmark
929.566
UserCPU time millis
10
UserCPU time millis training
10