Run
57906

Run 57906

Task 1731 (Learning Curve) pendigits Uploaded 14-11-2014 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.AdaBoostM1_REPTree(2)Yoav Freund, Robert E. Schapire: Experiments with a new boosting algorithm. In: Thirteenth International Conference on Machine Learning, San Francisco, 148-156, 1996.
weka.AdaBoostM1_REPTree(2)_I10
weka.AdaBoostM1_REPTree(2)_P100
weka.AdaBoostM1_REPTree(2)_S1
weka.AdaBoostM1_REPTree(2)_Wweka.classifiers.trees.REPTree
weka.REPTree(5)_I0.0
weka.REPTree(5)_L-1
weka.REPTree(5)_M2
weka.REPTree(5)_N3
weka.REPTree(5)_S1
weka.REPTree(5)_V0.001

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
0.9993 ± 0.0004
Per class
Build CPU time
0.2227
Build memory
2874585437.8
F measure
0.9881 ± 0.0033
Per class
Kappa
0.9868 ± 0.0036
KB relative information score
0.9882 ± 0.0027
Mean absolute error
0.0026 ± 0.0006
Mean prior absolute error
0.18 ± 0
Number of instances
10992
Per class
OS information
[Sun Microsystems Inc., 1.6.0_33, amd64, Linux, 3.5.0-54-generic]
Precision
0.9881 ± 0.0033
Per class
Predictive accuracy
0.9881 ± 0.0033
Prior entropy
3.3208 ± 0.0002
Recall
0.9881 ± 0.0033
Per class
Relative absolute error
0.0146 ± 0.0035
Root mean prior squared error
0.3 ± 0
Root mean squared error
0.0453 ± 0.0053
Root relative squared error
0.1511 ± 0.0177
SciMark benchmark
1614.7273