DEVELOPMENT... OpenML
Run
158241

Run 158241

Task 3877 (Supervised Classification) analcatdata_dmft 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)_I20
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
0.5531
Per class
F measure
0.7188
Per class
KB relative information score
-68.2357
Mean absolute error
0.3099
Mean prior absolute error
0.3138
Number of instances
797
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.6489
Per class
Predictive accuracy
0.8055
Prior entropy
0.7123
Recall
0.8055
Per class
Relative absolute error
0.9878
Root mean prior squared error
0.3958
Root mean squared error
0.3973
Root relative squared error
1.0039
SciMark benchmark
932.3943
UserCPU time millis
10
UserCPU time millis training
10