DEVELOPMENT... OpenML
Run
55163

Run 55163

Task 1740 (Learning Curve) soybean Uploaded 10-11-2014 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
0.9955 ± 0.0025
Per class
Build CPU time
0.028
Build memory
459791455.8
F measure
0.9206 ± 0.0236
Per class
Kappa
0.9164 ± 0.0418
KB relative information score
0.825 ± 0.0129
Mean absolute error
0.0332 ± 0.0017
Mean prior absolute error
0.0961 ± 0.0001
Number of instances
683
Per class
OS information
[Sun Microsystems Inc., 1.6.0_33, amd64, Linux, 3.5.0-54-generic]
Precision
0.9298 ± 0.0171
Per class
Predictive accuracy
0.9239 ± 0.038
Prior entropy
3.8358 ± 0.0227
Recall
0.9239 ± 0.038
Per class
Relative absolute error
0.3452 ± 0.0179
Root mean prior squared error
0.2191 ± 0.0002
Root mean squared error
0.1009 ± 0.0047
Root relative squared error
0.4603 ± 0.0216
SciMark benchmark
1606.2579