DEVELOPMENT... OpenML
Run
160024

Run 160024

Task 3650 (Supervised Classification) wind_correlations Uploaded 13-02-2015 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)_I80
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
0.917
Per class
F measure
0.8888
Per class
Kappa
0.7774
KB relative information score
34.518
Mean absolute error
0.1178
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
0.8896
Per class
Predictive accuracy
0.8889
Prior entropy
0.9997
Recall
0.8889
Per class
Relative absolute error
0.2357
Root mean prior squared error
0.4999
Root mean squared error
0.3349
Root relative squared error
0.6699
SciMark benchmark
924.6116
UserCPU time millis
30
UserCPU time millis training
30