DEVELOPMENT... OpenML
Run
161011

Run 161011

Task 3696 (Supervised Classification) autoMpg 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
0.9609
Per class
F measure
0.902
Per class
Kappa
0.8036
KB relative information score
311.8973
Mean absolute error
0.1097
Mean prior absolute error
0.4987
Number of instances
398
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.902
Per class
Predictive accuracy
0.902
Prior entropy
0.9982
Recall
0.902
Per class
Relative absolute error
0.22
Root mean prior squared error
0.4994
Root mean squared error
0.2865
Root relative squared error
0.5737
SciMark benchmark
936.6206
UserCPU time millis
100
UserCPU time millis training
100