DEVELOPMENT... OpenML
Run
157049

Run 157049

Task 3712 (Supervised Classification) wind 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
0.9225
Per class
F measure
0.8436
Per class
Kappa
0.6857
KB relative information score
3849.5349
Mean absolute error
0.2189
Mean prior absolute error
0.4979
Number of instances
6574
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.8436
Per class
Predictive accuracy
0.8436
Prior entropy
0.9969
Recall
0.8436
Per class
Relative absolute error
0.4396
Root mean prior squared error
0.4989
Root mean squared error
0.3344
Root relative squared error
0.6701
SciMark benchmark
929.566
UserCPU time millis
250
UserCPU time millis training
250