DEVELOPMENT... OpenML
Run
159375

Run 159375

Task 3761 (Supervised Classification) hip 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)_I40
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
0.919
Per class
F measure
0.8886
Per class
Kappa
0.7769
KB relative information score
38.0376
Mean absolute error
0.152
Mean prior absolute error
0.4993
Number of instances
54
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.8907
Per class
Predictive accuracy
0.8889
Prior entropy
0.9991
Recall
0.8889
Per class
Relative absolute error
0.3043
Root mean prior squared error
0.4997
Root mean squared error
0.3256
Root relative squared error
0.6516
SciMark benchmark
924.6116
UserCPU time millis
10
UserCPU time millis testing
10