DEVELOPMENT... OpenML
Run
159458

Run 159458

Task 3857 (Supervised Classification) vehicle 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
0.9949
Per class
F measure
0.9682
Per class
Kappa
0.917
KB relative information score
745.0196
Mean absolute error
0.046
Mean prior absolute error
0.3828
Number of instances
846
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.9683
Per class
Predictive accuracy
0.9681
Prior entropy
0.8241
Recall
0.9681
Per class
Relative absolute error
0.1201
Root mean prior squared error
0.4374
Root mean squared error
0.1448
Root relative squared error
0.3311
SciMark benchmark
945.6434
UserCPU time millis
130
UserCPU time millis training
130