Random forest computes similarity between instances with classification of out-of-bag instances. If two out-of-bag cases are classified in the same tree leaf the proximity between them is incremented.

1 | ```
rfProximity(model, outProximity=TRUE)
``` |

`model` |
a |

`outProximity` |
if |

A proximity is transformed into distance with expression `distance=sqrt(1-proximity)`

.

Function returns an M by M matrix where M is the number of training instances.
Returned matrix is used as an input to other function (see `rfOutliers`

and `rfClustering`

).

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`

,
`rfOutliers`

,
`cmdscale`

,
`rfClustering`

.

1 2 3 4 5 6 7 | ```
md <- CoreModel(Species ~ ., iris, model="rf", rfNoTrees=30, maxThreads=1)
pr <- rfProximity(md, outProximity=TRUE)
# visualization
require(lattice)
levelplot(pr)
destroyModels(md) # clean up
``` |

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