DEVELOPMENT... OpenML
Run
160394

Run 160394

Task 3535 (Supervised Classification) tr41.wc 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
0.9969
Per class
F measure
0.9789
Per class
Kappa
0.9751
KB relative information score
849.6985
Mean absolute error
0.0041
Mean prior absolute error
0.1652
Number of instances
878
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.9798
Per class
Predictive accuracy
0.9795
Prior entropy
2.8041
Recall
0.9795
Per class
Relative absolute error
0.0251
Root mean prior squared error
0.2872
Root mean squared error
0.0602
Root relative squared error
0.2094
SciMark benchmark
941.7954
UserCPU time millis
4723790
UserCPU time millis training
4723790