DEVELOPMENT... OpenML
Run
160087

Run 160087

Task 3725 (Supervised Classification) auto93 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
0.9273
Per class
F measure
0.8588
Per class
Kappa
0.6971
KB relative information score
62.7443
Mean absolute error
0.1498
Mean prior absolute error
0.4701
Number of instances
93
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.8594
Per class
Predictive accuracy
0.8602
Prior entropy
0.9573
Recall
0.8602
Per class
Relative absolute error
0.3188
Root mean prior squared error
0.4845
Root mean squared error
0.3434
Root relative squared error
0.7089
SciMark benchmark
924.6116
UserCPU time millis
20
UserCPU time millis training
20