Run
53773

Run 53773

Task 1713 (Learning Curve) mfeat-fourier Uploaded 08-11-2014 by Jan van Rijn
0 likes downloaded by 0 people 0 issues 0 downvotes , 0 total downloads
  • curves study_2 study_37 weka weka_3.7.12-SNAPSHOT
Issue #Downvotes for this reason By


Flow

weka.MultiBoostAB_REPTree(2)Geoffrey I. Webb (2000). MultiBoosting: A Technique for Combining Boosting and Wagging. Machine Learning. Vol.40(No.2).
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
weka.MultiBoostAB_REPTree(2)_C3
weka.MultiBoostAB_REPTree(2)_P100
weka.MultiBoostAB_REPTree(2)_S1
weka.MultiBoostAB_REPTree(2)_I10
weka.MultiBoostAB_REPTree(2)_Wweka.classifiers.trees.REPTree

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
0.9697 ± 0.0053
Per class
Build CPU time
0.6925
Build memory
2811765020.7273
F measure
0.8082 ± 0.0286
Per class
Kappa
0.7883 ± 0.0307
KB relative information score
0.812 ± 0.0212
Mean absolute error
0.04 ± 0.0039
Mean prior absolute error
0.18
Number of instances
2000
Per class
OS information
[Sun Microsystems Inc., 1.6.0_33, amd64, Linux, 3.5.0-54-generic]
Precision
0.8103 ± 0.0287
Per class
Predictive accuracy
0.8095 ± 0.0276
Prior entropy
3.3219
Recall
0.8095 ± 0.0276
Per class
Relative absolute error
0.2223 ± 0.0217
Root mean prior squared error
0.3
Root mean squared error
0.1787 ± 0.0117
Root relative squared error
0.5956 ± 0.0391
SciMark benchmark
1624.8488