OpenML
435237

Run 435237

Task 86 (Learning Curve) colic Uploaded 12-05-2015 by Joaquin Vanschoren
0 likes downloaded by 0 people 0 issues 0 downvotes , 0 total downloads
Issue #Downvotes for this reason By


Flow

weka.RandomSubSpace_REPTree(4)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(9)_M2
weka.REPTree(9)_V0.001
weka.REPTree(9)_N3
weka.REPTree(9)_S1
weka.REPTree(9)_L-1
weka.REPTree(9)_I0.0
weka.RandomSubSpace_REPTree(4)_P0.5
weka.RandomSubSpace_REPTree(4)_S1
weka.RandomSubSpace_REPTree(4)_num-slots1
weka.RandomSubSpace_REPTree(4)_I160
weka.RandomSubSpace_REPTree(4)_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.

22 Evaluation measures

Area under ROC curve
0.8956 ± 0.0589
Per class
F measure
0.8449 ± 0.0608
Per class
Kappa
0.6622 ± 0.1319
KB relative information score
0.3777 ± 0.0566
Mean absolute error
0.3142 ± 0.0215
Mean prior absolute error
0.4662 ± 0.0029
Number of instances
3680
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.8507 ± 0.055
Per class
Predictive accuracy
0.8489 ± 0.0555
Prior entropy
0.9503 ± 0.0087
Recall
0.8489 ± 0.0555
Per class
Relative absolute error
0.6739 ± 0.0456
Root mean prior squared error
0.4827 ± 0.0031
Root mean squared error
0.3598 ± 0.03
Root relative squared error
0.7455 ± 0.0616
SciMark benchmark
921.1883
UserCPU time millis
440
UserCPU time millis testing
30
UserCPU time millis training
410