OpenML
292896

Run 292896

Task 94 (Learning Curve) dermatology Uploaded 17-03-2015 by Joaquin Vanschoren
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Flow

weka.REPTree(9)Weka implementation of REPTree
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

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
0.9681 ± 0.0203
Per class
F measure
0.9075 ± 0.0401
Per class
Kappa
0.8841 ± 0.0498
KB relative information score
0.8641 ± 0.0444
Mean absolute error
0.0421 ± 0.0119
Mean prior absolute error
0.2664 ± 0.0004
Number of instances
3660
Per class
OS information
[Oracle Corporation, 1.7.0_75, amd64, Linux, 3.16.0-29-generic]
Precision
0.9093 ± 0.0345
Per class
Predictive accuracy
0.9079 ± 0.0395
Prior entropy
2.4327 ± 0.0089
Recall
0.9079 ± 0.0395
Per class
Relative absolute error
0.158 ± 0.0446
Root mean prior squared error
0.3649 ± 0.0006
Root mean squared error
0.1629 ± 0.0347
Root relative squared error
0.4466 ± 0.095
SciMark benchmark
1338.2217
UserCPU time millis
10
UserCPU time millis training
10