DEVELOPMENT... OpenML
Run
160937

Run 160937

Task 3622 (Supervised Classification) autoPrice Uploaded 15-02-2015 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)_I160
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
0.9759
Per class
F measure
0.9747
Per class
Kappa
0.9434
KB relative information score
149.0231
Mean absolute error
0.0264
Mean prior absolute error
0.4492
Number of instances
159
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.975
Per class
Predictive accuracy
0.9748
Prior entropy
0.9264
Recall
0.9748
Per class
Relative absolute error
0.0589
Root mean prior squared error
0.4736
Root mean squared error
0.1591
Root relative squared error
0.336
SciMark benchmark
936.6206
UserCPU time millis
60
UserCPU time millis training
60