DEVELOPMENT... OpenML
Run
159929

Run 159929

Task 3542 (Supervised Classification) analcatdata_lawsuit 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
0.9901
Per class
F measure
0.9852
Per class
Kappa
0.8918
KB relative information score
211.7914
Mean absolute error
0.0176
Mean prior absolute error
0.1363
Number of instances
264
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.9859
Per class
Predictive accuracy
0.9848
Prior entropy
0.385
Recall
0.9848
Per class
Relative absolute error
0.1289
Root mean prior squared error
0.2585
Root mean squared error
0.1222
Root relative squared error
0.473
SciMark benchmark
938.2848
UserCPU time millis
20
UserCPU time millis training
20