Description Usage Arguments Details Value Author(s) References See Also Examples

Based on random forest instance proximity measure detects training cases which are different to all other cases.

1 | ```
rfOutliers(model, dataset)
``` |

`model` |
a random forest model returned by |

`dataset` |
a training set used to generate the |

Strangeness is defined using the random forest model via a proximity matrix (see `rfProximity`

).
If the number is greater than 10, the case can be considered an outlier according to Breiman 2001.

For each instance from a `dataset`

the function returns a numeric score of its strangeness to other cases.

John Adeyanju Alao (as a part of his BSc thesis) and Marko Robnik-Sikonja (thesis supervisor)

Leo Breiman: Random Forests. *Machine Learning Journal*, 45:5-32, 2001

`CoreModel`

,
`rfProximity`

,
`rfClustering`

.

1 2 3 4 5 6 7 8 9 10 | ```
#first create a random forest tree using CORElearn
dataset <- iris
md <- CoreModel(Species ~ ., dataset, model="rf", rfNoTrees=30,
maxThreads=1)
outliers <- rfOutliers(md, dataset)
plot(abs(outliers))
#for a nicer display try
plot(md, dataset, rfGraphType="outliers")
destroyModels(md) # clean up
``` |

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