DEVELOPMENT... OpenML
Run
160374

Run 160374

Task 2078 (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)_I160
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
0.6511
Per class
F measure
0.7055
Per class
Kappa
0.1732
KB relative information score
54.2658
Mean absolute error
0.0878
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
0.6857
Per class
Predictive accuracy
0.733
Prior entropy
1.1781
Recall
0.733
Per class
Relative absolute error
0.796
Root mean prior squared error
0.2325
Root mean squared error
0.251
Root relative squared error
1.0798
SciMark benchmark
938.4285
UserCPU time millis
520
UserCPU time millis testing
10
UserCPU time millis training
510