DEVELOPMENT... OpenML
Run
157542

Run 157542

Task 2257 (Supervised Classification) BNG(cylinder-bands) 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
0.8353
Per class
F measure
0.7588
Per class
Kappa
0.5025
KB relative information score
335847.7454
Mean absolute error
0.3375
Mean prior absolute error
0.4878
Number of instances
1000000
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.762
Per class
Predictive accuracy
0.7624
Prior entropy
0.9823
Recall
0.7624
Per class
Relative absolute error
0.6919
Root mean prior squared error
0.4939
Root mean squared error
0.4021
Root relative squared error
0.8143
SciMark benchmark
933.1776
UserCPU time millis
447550
UserCPU time millis testing
900
UserCPU time millis training
446650