DEVELOPMENT... OpenML
Run
159255

Run 159255

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)_I40
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
0.9815
Per class
F measure
0.9811
Per class
Kappa
0.9577
KB relative information score
150.5049
Mean absolute error
0.0229
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
0.051
Root mean prior squared error
0.4736
Root mean squared error
0.1409
Root relative squared error
0.2974
SciMark benchmark
924.6116
UserCPU time millis
10
UserCPU time millis training
10