DEVELOPMENT... OpenML
Run
160167

Run 160167

Task 3820 (Supervised Classification) braziltourism 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
0.6907
Per class
F measure
0.7517
Per class
Kappa
0.2457
KB relative information score
49.643
Mean absolute error
0.2876
Mean prior absolute error
0.3529
Number of instances
412
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.7483
Per class
Predictive accuracy
0.7791
Prior entropy
0.7771
Recall
0.7791
Per class
Relative absolute error
0.815
Root mean prior squared error
0.4196
Root mean squared error
0.4105
Root relative squared error
0.9782
SciMark benchmark
924.6116
UserCPU time millis
40
UserCPU time millis training
40