# predict.extraTrees: Function for making predictions from trained ExtraTree... In extraTrees: Extremely Randomized Trees (ExtraTrees) Method for Classification and Regression

## Description

This function makes predictions for regression/classification using the given trained ExtraTree object and provided input matrix (newdata).

## Usage

 ```1 2``` ``` ## S3 method for class 'extraTrees' predict(object, newdata, quantile=NULL, allValues=F, probability=F, newtasks=NULL, ...) ```

## Arguments

 `object` extraTree (S3) object, created by extraTrees(). `newdata` a new numberic input data matrix, for each row a prediction is made. `quantile` the quantile value between 0.0 and 1.0 for quantile regression, or NULL (default) for standard predictions. `allValues` whether or not to return outputs of all trees (default FALSE). `probability` whether to return a matrix of class (factor) probabilities, default FALSE. Can only be used in the case of classification. Calculated as the proportion of trees voting for particular class. `newtasks` list of tasks, for each input in newdata (default NULL). Must be NULL if no multi-task learning was used at training. `...` not used currently.

## Value

The vector of predictions from the ExtraTree et. The length of the vector is equal to the the number of rows in newdata.

Jaak Simm

## Examples

 ```1 2 3 4 5 6 7``` ``` ## Regression with ExtraTrees: n <- 1000 ## number of samples p <- 5 ## number of dimensions x <- matrix(runif(n*p), n, p) y <- (x[,1]>0.5) + 0.8*(x[,2]>0.6) + 0.5*(x[,3]>0.4) + 0.1*runif(nrow(x)) et <- extraTrees(x, y, nodesize=3, mtry=p, numRandomCuts=2) yhat <- predict(et, x) ```

extraTrees documentation built on May 2, 2019, 2:31 p.m.