DEVELOPMENT... OpenML
Run
161164

Run 161164

Task 3849 (Supervised Classification) analcatdata_marketing Uploaded 15-02-2015 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
0.5212
Per class
F measure
0.5792
Per class
Kappa
0.0149
KB relative information score
-21.6693
Mean absolute error
0.4326
Mean prior absolute error
0.4326
Number of instances
364
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.5742
Per class
Predictive accuracy
0.5852
Prior entropy
0.901
Recall
0.5852
Per class
Relative absolute error
0.9999
Root mean prior squared error
0.4649
Root mean squared error
0.545
Root relative squared error
1.1723
SciMark benchmark
938.4285
UserCPU time millis
110
UserCPU time millis training
110