predict.randomForest {randomForest} | R Documentation |

Prediction of test data using random forest.

## S3 method for class 'randomForest': predict(object, newdata, type="response", norm.votes=TRUE, predict.all=FALSE, proximity=FALSE, nodes=FALSE, ...)

`object` |
an object of class `randomForest` , as that
created by the function `randomForest` . |

`newdata` |
a data frame or matrix containing new data. (Note: If
not given, the out-of-bag prediction in `object` is returned. |

`type` |
one of `response` , `prob` . or `votes` ,
indicating the type of output: predicted values, matrix of class
probabilities, or matrix of vote counts. `class` is allowed, but
automatically converted to "response", for backward compatibility. |

`norm.votes` |
Should the vote counts be normalized (i.e.,
expressed as fractions)? Ignored if `object$type` is
`regression` . |

`predict.all` |
Should the predictions of all trees be kept? |

`proximity` |
Should proximity measures be computed? An error is
issued if `object$type` is `regression` . |

`nodes` |
Should the terminal node indicators (an n by ntree matrix) be return? If so, it is in the ``nodes'' attribute of the returned object. |

`...` |
not used currently. |

If `object$type`

is `regression`

, a vector of predicted
values is returned. If `predict.all=TRUE`

, then the returned
object is a list of two components: `aggregate`

, which is the
vector of predicted values by the forest, and `individual`

, which
is a matrix where each column contains prediction by a tree in the
forest.

If `object$type`

is `classification`

, the object returned
depends on the argument `type`

:

`response` |
predicted classes (the classes with majority vote). |

`prob` |
matrix of class probabilities (one column for each class and one row for each input). |

`vote` |
matrix of vote counts (one column for each class
and one row for each new input); either in raw counts or in fractions
(if `norm.votes=TRUE` ). |

If `predict.all=TRUE`

, then the `individual`

component of the
returned object is a character matrix where each column contains the
predicted class by a tree in the forest.

If `proximity=TRUE`

, the returned object is a list with two
components: `pred`

is the prediction (as described above) and
`proximity`

is the proximitry matrix. An error is issued if
`object$type`

is `regression`

.

If `nodes=TRUE`

, the returned object has a ``nodes'' attribute,
which is an n by ntree matrix, each column containing the node number
that the cases fall in for that tree.

Andy Liaw andy_liaw@merck.com and Matthew Wiener matthew_wiener@merck.com, based on original Fortran code by Leo Breiman and Adele Cutler.

Breiman, L. (2001), *Random Forests*, Machine Learning 45(1),
5-32.

data(iris) set.seed(111) ind <- sample(2, nrow(iris), replace = TRUE, prob=c(0.8, 0.2)) iris.rf <- randomForest(Species ~ ., data=iris[ind == 1,]) iris.pred <- predict(iris.rf, iris[ind == 2,]) table(observed = iris[ind==2, "Species"], predicted = iris.pred)

[Package *randomForest* version 4.5-1 Index]