OpenML
435244

Run 435244

Task 97 (Learning Curve) ecoli Uploaded 12-05-2015 by Joaquin Vanschoren
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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.

20 Evaluation measures

Area under ROC curve
0.9638 ± 0.0193
Per class
Kappa
0.7864 ± 0.0705
KB relative information score
0.6431 ± 0.0508
Mean absolute error
0.0866 ± 0.0062
Mean prior absolute error
0.1828 ± 0.0012
Number of instances
3360
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Predictive accuracy
0.8491 ± 0.0491
Prior entropy
2.1906 ± 0.0868
Recall
0.8491 ± 0.0491
Per class
Relative absolute error
0.4735 ± 0.0327
Root mean prior squared error
0.3016 ± 0.0021
Root mean squared error
0.1828 ± 0.016
Root relative squared error
0.606 ± 0.0515
SciMark benchmark
942.2965
UserCPU time millis
200
UserCPU time millis testing
40
UserCPU time millis training
160