DEVELOPMENT... OpenML
Run
160084

Run 160084

Task 3722 (Supervised Classification) hungarian 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
0.8623
Per class
F measure
0.784
Per class
Kappa
0.5285
KB relative information score
150.2159
Mean absolute error
0.224
Mean prior absolute error
0.4614
Number of instances
294
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.7833
Per class
Predictive accuracy
0.7857
Prior entropy
0.9439
Recall
0.7857
Per class
Relative absolute error
0.4855
Root mean prior squared error
0.4802
Root mean squared error
0.3839
Root relative squared error
0.7994
SciMark benchmark
924.6116
UserCPU time millis
40
UserCPU time millis training
40