DEVELOPMENT... OpenML
Run
160185

Run 160185

Task 3837 (Supervised Classification) hayes-roth 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
0.942
Per class
F measure
0.7799
Per class
Kappa
0.535
KB relative information score
85.5712
Mean absolute error
0.1617
Mean prior absolute error
0.4746
Number of instances
132
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.7796
Per class
Predictive accuracy
0.7803
Prior entropy
0.9635
Recall
0.7803
Per class
Relative absolute error
0.3407
Root mean prior squared error
0.4869
Root mean squared error
0.2886
Root relative squared error
0.5927
SciMark benchmark
924.6116
UserCPU time millis
10
UserCPU time millis training
10