# tree.predictions

From mobForest v1.3.1
by Kasey Jones

##### Predictions from tree model

This method computes predicted outcome for each observation in the data frame using the tree model supplied as an input argument.

##### Usage

`tree.predictions(j, df, tree)`

##### Arguments

##### Value

A vector of predicted outcome

##### Examples

```
# NOT RUN {
library(mlbench)
set.seed(1111)
# Random Forest analysis of model based recursive partitioning load data
data("BostonHousing", package = "mlbench")
data <- BostonHousing[1:90, c("rad", "tax", "crim", "medv", "lstat")]
fmBH <- mob.rf.tree(main_model = "medv ~ lstat",
partition_vars = c("rad", "tax", "crim"), mtry = 2,
control = mob_control(), data = data,
model = linearModel)
tree.predictions(j = 1, df = data, tree = fmBH@tree)
# }
```

*Documentation reproduced from package mobForest, version 1.3.1, License: GPL (>= 2)*

### Community examples

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