DEVELOPMENT... OpenML
Run
159040

Run 159040

Task 2078 (Supervised Classification) braziltourism 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
0.6674
Per class
F measure
0.6931
Per class
Kappa
0.1254
KB relative information score
45.9612
Mean absolute error
0.0922
Mean prior absolute error
0.1103
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
0.661
Per class
Predictive accuracy
0.7403
Prior entropy
1.1781
Recall
0.7403
Per class
Relative absolute error
0.8358
Root mean prior squared error
0.2325
Root mean squared error
0.2391
Root relative squared error
1.0283
SciMark benchmark
924.6116
UserCPU time millis
100
UserCPU time millis training
100