DEVELOPMENT... OpenML
Run
51823

Run 51823

Task 1748 (Learning Curve) tic-tac-toe Uploaded 07-11-2014 by Jason
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
0.9779 ± 0.011
Per class
Build CPU time
0.0072
Build memory
405438644.0889
F measure
0.9138 ± 0.0246
Per class
Kappa
0.8072 ± 0.0555
KB relative information score
0.8048 ± 0.0475
Mean absolute error
0.0857 ± 0.0199
Mean prior absolute error
0.453 ± 0.0013
Number of instances
958
Per class
OS information
[Sun Microsystems Inc., 1.6.0_33, amd64, Linux, 3.5.0-54-generic]
Precision
0.9169 ± 0.0227
Per class
Predictive accuracy
0.9154 ± 0.0235
Prior entropy
0.931 ± 0.0039
Recall
0.9154 ± 0.0235
Per class
Relative absolute error
0.1891 ± 0.0439
Root mean prior squared error
0.4759 ± 0.0014
Root mean squared error
0.2572 ± 0.0379
Root relative squared error
0.5405 ± 0.0797
SciMark benchmark
1638.2006