DEVELOPMENT... OpenML
Run
160844

Run 160844

Task 3532 (Supervised Classification) tr12.wc Uploaded 15-02-2015 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
0.9296
Per class
F measure
0.8099
Per class
Kappa
0.7753
KB relative information score
246.6982
Mean absolute error
0.0449
Mean prior absolute error
0.2079
Number of instances
313
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.8144
Per class
Predictive accuracy
0.8147
Prior entropy
2.7783
Recall
0.8147
Per class
Relative absolute error
0.2157
Root mean prior squared error
0.3222
Root mean squared error
0.2065
Root relative squared error
0.641
SciMark benchmark
939.7623
UserCPU time millis
2006060
UserCPU time millis training
2006060