DEVELOPMENT... OpenML
Run
159133

Run 159133

Task 3507 (Supervised Classification) vehicle_sensIT 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
0.913
Per class
F measure
0.8571
Per class
Kappa
0.7149
KB relative information score
59477.2343
Mean absolute error
0.2117
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
0.8611
Per class
Predictive accuracy
0.8575
Prior entropy
1
Recall
0.8575
Per class
Relative absolute error
0.4234
Root mean prior squared error
0.5
Root mean squared error
0.3282
Root relative squared error
0.6563
SciMark benchmark
932.3943
UserCPU time millis
747340
UserCPU time millis testing
180
UserCPU time millis training
747160