DEVELOPMENT... OpenML
Run
157981

Run 157981

Task 3622 (Supervised Classification) autoPrice 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)_I20
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
0.9924
Per class
F measure
0.9811
Per class
Kappa
0.9577
KB relative information score
149.1492
Mean absolute error
0.0268
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
0.9811
Per class
Predictive accuracy
0.9811
Prior entropy
0.9264
Recall
0.9811
Per class
Relative absolute error
0.0596
Root mean prior squared error
0.4736
Root mean squared error
0.1457
Root relative squared error
0.3077
SciMark benchmark
947.9494
UserCPU time millis
10
UserCPU time millis training
10