DEVELOPMENT... OpenML
Run
160831

Run 160831

Task 3507 (Supervised Classification) vehicle_sensIT 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
0.9189
Per class
F measure
0.8651
Per class
Kappa
0.7308
KB relative information score
61075.6867
Mean absolute error
0.2035
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
0.8689
Per class
Predictive accuracy
0.8654
Prior entropy
1
Recall
0.8654
Per class
Relative absolute error
0.4069
Root mean prior squared error
0.5
Root mean squared error
0.3205
Root relative squared error
0.641
SciMark benchmark
940.2922
UserCPU time millis
2965070
UserCPU time millis testing
180
UserCPU time millis training
2964890