DEVELOPMENT... OpenML
Run
160064

Run 160064

Task 3696 (Supervised Classification) autoMpg Uploaded 13-02-2015 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)_I80
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
0.9649
Per class
F measure
0.8894
Per class
Kappa
0.7783
KB relative information score
309.8044
Mean absolute error
0.1123
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
0.8894
Per class
Predictive accuracy
0.8894
Prior entropy
0.9982
Recall
0.8894
Per class
Relative absolute error
0.2252
Root mean prior squared error
0.4994
Root mean squared error
0.2759
Root relative squared error
0.5526
SciMark benchmark
924.6116
UserCPU time millis
60
UserCPU time millis training
60