DEVELOPMENT... OpenML
Run
157784

Run 157784

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)_I20
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
0.9064
Per class
F measure
0.85
Per class
Kappa
0.7008
KB relative information score
58027.2243
Mean absolute error
0.2188
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
0.8544
Per class
Predictive accuracy
0.8504
Prior entropy
1
Recall
0.8504
Per class
Relative absolute error
0.4376
Root mean prior squared error
0.5
Root mean squared error
0.336
Root relative squared error
0.6719
SciMark benchmark
923.3111
UserCPU time millis
398520
UserCPU time millis testing
160
UserCPU time millis training
398360