DEVELOPMENT... OpenML
Run
160233

Run 160233

Task 3887 (Supervised Classification) analcatdata_germangss 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
0.9585
Per class
F measure
0.9038
Per class
Kappa
0.721
KB relative information score
258.1646
Mean absolute error
0.1176
Mean prior absolute error
0.3495
Number of instances
400
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.9032
Per class
Predictive accuracy
0.905
Prior entropy
0.7716
Recall
0.905
Per class
Relative absolute error
0.3364
Root mean prior squared error
0.4176
Root mean squared error
0.2563
Root relative squared error
0.6137
SciMark benchmark
924.6116
UserCPU time millis
20
UserCPU time millis training
20