Run
52144

Run 52144

Task 1728 (Learning Curve) credit-approval Uploaded 07-11-2014 by Jan van Rijn
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  • curves study_2 study_37 weka weka_3.7.12-SNAPSHOT
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Flow

weka.RealAdaBoost_MultilayerPerceptron(1)J. Friedman, T. Hastie, R. Tibshirani (2000). Additive Logistic Regression: a Statistical View of Boosting. Annals of Statistics. 95(2):337-407.
weka.MultilayerPerceptron(2)_E20
weka.MultilayerPerceptron(2)_Ha
weka.MultilayerPerceptron(2)_L0.3
weka.MultilayerPerceptron(2)_M0.2
weka.MultilayerPerceptron(2)_N500
weka.MultilayerPerceptron(2)_S0
weka.MultilayerPerceptron(2)_V0
weka.RealAdaBoost_MultilayerPerceptron(1)_P100
weka.RealAdaBoost_MultilayerPerceptron(1)_H1.0
weka.RealAdaBoost_MultilayerPerceptron(1)_S1
weka.RealAdaBoost_MultilayerPerceptron(1)_I10
weka.RealAdaBoost_MultilayerPerceptron(1)_Wweka.classifiers.functions.MultilayerPerceptron

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.

21 Evaluation measures

Area under ROC curve
0.8521 ± 0.0435
Per class
Build CPU time
7.1623
Build memory
264031298
F measure
0.6801 ± 0.2212
Per class
Kappa
0.3685 ± 0.3729
KB relative information score
0.4491 ± 0.2442
Mean absolute error
0.2683 ± 0.1158
Mean prior absolute error
0.494 ± 0.0008
Number of instances
690
Per class
OS information
[Sun Microsystems Inc., 1.6.0_33, amd64, Linux, 3.5.0-54-generic]
Precision
0.735 ± 0.2098
Per class
Predictive accuracy
0.7029 ± 0.1595
Prior entropy
0.9912 ± 0.0022
Recall
0.7029 ± 0.1595
Per class
Relative absolute error
0.5432 ± 0.2344
Root mean prior squared error
0.497 ± 0.0008
Root mean squared error
0.4769 ± 0.1087
Root relative squared error
0.9597 ± 0.2186
SciMark benchmark
1629.4508