DEVELOPMENT... OpenML
Run
160079

Run 160079

Task 3717 (Supervised Classification) analcatdata_gsssexsurvey 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
0.5327
Per class
F measure
0.6807
Per class
Kappa
0.0017
KB relative information score
-15.7379
Mean absolute error
0.3334
Mean prior absolute error
0.3453
Number of instances
159
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.6573
Per class
Predictive accuracy
0.717
Prior entropy
0.7667
Recall
0.717
Per class
Relative absolute error
0.9654
Root mean prior squared error
0.4143
Root mean squared error
0.4672
Root relative squared error
1.1275
SciMark benchmark
924.6116
UserCPU time millis
20
UserCPU time millis training
20