DEVELOPMENT... OpenML
Run
160265

Run 160265

Task 3917 (Supervised Classification) kc1 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)_I80
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
0.7953
Per class
F measure
0.8235
Per class
Kappa
0.24
KB relative information score
102.4373
Mean absolute error
0.2088
Mean prior absolute error
0.2616
Number of instances
2109
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.8273
Per class
Predictive accuracy
0.8554
Prior entropy
0.622
Recall
0.8554
Per class
Relative absolute error
0.7981
Root mean prior squared error
0.3615
Root mean squared error
0.3307
Root relative squared error
0.9149
SciMark benchmark
943.2817
UserCPU time millis
610
UserCPU time millis testing
10
UserCPU time millis training
600