DEVELOPMENT... OpenML
Run
59059

Run 59059

Task 1705 (Learning Curve) letter Uploaded 15-11-2014 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.RandomSubSpace_REPTree(2)Tin Kam Ho (1998). The Random Subspace Method for Constructing Decision Forests. IEEE Transactions on Pattern Analysis and Machine Intelligence. 20(8):832-844. URL http://citeseer.ist.psu.edu/ho98random.html.
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.RandomSubSpace_REPTree(2)_P0.5
weka.RandomSubSpace_REPTree(2)_S1
weka.RandomSubSpace_REPTree(2)_num-slots1
weka.RandomSubSpace_REPTree(2)_I10
weka.RandomSubSpace_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
0.9967 ± 0.0006
Per class
Build CPU time
0.2562
Build memory
2264456418.3556
F measure
0.9145 ± 0.0055
Per class
Kappa
0.9109 ± 0.0059
KB relative information score
0.8342 ± 0.0097
Mean absolute error
0.0254 ± 0.0013
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
0.9158 ± 0.0052
Per class
Predictive accuracy
0.9143 ± 0.0057
Prior entropy
4.6998 ± 0.0001
Recall
0.9143 ± 0.0057
Per class
Relative absolute error
0.3433 ± 0.0173
Root mean prior squared error
0.1923 ± 0
Root mean squared error
0.0915 ± 0.0026
Root relative squared error
0.476 ± 0.0136
SciMark benchmark
1769.7895