DEVELOPMENT... OpenML
Run
160272

Run 160272

Task 3923 (Supervised Classification) datatrieve 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
0.7647
Per class
F measure
0.8643
Per class
Kappa
0.0485
KB relative information score
-18.8385
Mean absolute error
0.128
Mean prior absolute error
0.1601
Number of instances
130
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.8531
Per class
Predictive accuracy
0.8769
Prior entropy
0.4395
Recall
0.8769
Per class
Relative absolute error
0.7993
Root mean prior squared error
0.2784
Root mean squared error
0.3376
Root relative squared error
1.2126
SciMark benchmark
943.2817
UserCPU time millis
20
UserCPU time millis training
20