DEVELOPMENT... OpenML
Run
157142

Run 157142

Task 3804 (Supervised Classification) lowbwt Uploaded 11-02-2015 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)_I10
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
0.8933
Per class
F measure
0.8391
Per class
Kappa
0.6789
KB relative information score
109.6436
Mean absolute error
0.2228
Mean prior absolute error
0.4989
Number of instances
189
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.8529
Per class
Predictive accuracy
0.8413
Prior entropy
0.9984
Recall
0.8413
Per class
Relative absolute error
0.4466
Root mean prior squared error
0.4994
Root mean squared error
0.3321
Root relative squared error
0.665
SciMark benchmark
931.2336
UserCPU time millis
10
UserCPU time millis training
10