DEVELOPMENT... OpenML
Run
156852

Run 156852

Task 3507 (Supervised Classification) vehicle_sensIT Uploaded 11-02-2015 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)_I10
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
0.8981
Per class
F measure
0.8401
Per class
Kappa
0.6812
KB relative information score
55783.6811
Mean absolute error
0.2303
Mean prior absolute error
0.5
Number of instances
98528
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.8452
Per class
Predictive accuracy
0.8406
Prior entropy
1
Recall
0.8406
Per class
Relative absolute error
0.4606
Root mean prior squared error
0.5
Root mean squared error
0.3452
Root relative squared error
0.6903
SciMark benchmark
940.2922
UserCPU time millis
183140
UserCPU time millis testing
170
UserCPU time millis training
182970