DEVELOPMENT... OpenML
Run
160333

Run 160333

Task 21 (Supervised Classification) car Uploaded 13-02-2015 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
0.99
Per class
F measure
0.9345
Per class
Kappa
0.8574
KB relative information score
1444.4063
Mean absolute error
0.0465
Mean prior absolute error
0.229
Number of instances
1728
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.9352
Per class
Predictive accuracy
0.934
Prior entropy
1.2099
Recall
0.934
Per class
Relative absolute error
0.2033
Root mean prior squared error
0.3381
Root mean squared error
0.1511
Root relative squared error
0.4469
SciMark benchmark
933.8635
UserCPU time millis
490
UserCPU time millis training
490