DEVELOPMENT... OpenML
Run
157192

Run 157192

Task 3852 (Supervised Classification) anneal 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
0.9853
Per class
F measure
0.9823
Per class
Kappa
0.9517
KB relative information score
743.0068
Mean absolute error
0.0744
Mean prior absolute error
0.3633
Number of instances
898
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.9828
Per class
Predictive accuracy
0.9822
Prior entropy
0.7932
Recall
0.9822
Per class
Relative absolute error
0.2046
Root mean prior squared error
0.426
Root mean squared error
0.1446
Root relative squared error
0.3394
SciMark benchmark
929.566
UserCPU time millis
40
UserCPU time millis training
40