DEVELOPMENT... OpenML
Run
55403

Run 55403

Task 1705 (Learning Curve) letter Uploaded 11-11-2014 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
0.9965 ± 0.0006
Per class
Build CPU time
0.5773
Build memory
2049822646.0889
F measure
0.9209 ± 0.0055
Per class
Kappa
0.9176 ± 0.0058
KB relative information score
0.9294 ± 0.0052
Mean absolute error
0.0068 ± 0.0004
Mean prior absolute error
0.074 ± 0
Number of instances
20000
Per class
OS information
[Sun Microsystems Inc., 1.6.0_33, amd64, Linux, 3.5.0-54-generic]
Precision
0.9215 ± 0.0054
Per class
Predictive accuracy
0.9208 ± 0.0055
Prior entropy
4.6998 ± 0.0001
Recall
0.9208 ± 0.0055
Per class
Relative absolute error
0.0914 ± 0.0056
Root mean prior squared error
0.1923 ± 0
Root mean squared error
0.0697 ± 0.0029
Root relative squared error
0.3625 ± 0.0149
SciMark benchmark
1711.0442