# predictive.acc

##### Predictive performance across all trees

Predictive performance across all trees

##### Usage

```
predictive.acc(object = "mfOutput", newdata = F, prob_cutoff = NULL,
plot = T)
```

##### Arguments

- object
An object of class

`mobforest.output`

- newdata
A logical value specifying if the performance needs to be summarized on test data supplied as

`new_test_data`

argument to`mobforest.analysis`

function.- prob_cutoff
Predicted probabilities converted into classes (Yes/No, 1/0) based on this probability threshold. Only used for producing predicted Vs actual classes table.

- plot
A logical value specifying if the use wishes to view performance plots

##### Value

A list with performance parameters

A vector of predictive accuracy estimates (ranging between 0 and 1) measured on Out-of-bag cases for each tree

A vector of MSE for Out-of-bag data for each tree. Valid only if the outcome is continuous.

Overall predictive accuracy measured by combining Out-of-bag predictions across trees.

Overall MSE measured by combining Out-of-bag predictions across trees.

A vector of predictive accuracy (ranging between 0 and 1) measured on complete learning data for each tree

A vector of MSE measured on complete learning data for each tree. Valid only if the outcome is continuous.

Overall predictive accuracy measured by combining predictions across trees.

Overall MSE measured by combining predictions across trees. Valid only if the outcome is continuous.

The node model and partition variables used for analysis

Error distribution assumptions of the model

##### Examples

```
# NOT RUN {
library(mlbench)
set.seed(1111)
# Random Forest analysis of model based recursive partitioning load data
data("BostonHousing", package = "mlbench")
BostonHousing <- BostonHousing[1:90, c("rad", "tax", "crim", "medv", "lstat")]
# Recursive partitioning based on linear regression model medv ~ lstat with 3
# trees. 1 core/processor used.
rfout <- mobforest.analysis(as.formula(medv ~ lstat), c("rad", "tax", "crim"),
mobforest_controls = mobforest.control(ntree = 3, mtry = 2, replace = T,
alpha = 0.05, bonferroni = T, minsplit = 25), data = BostonHousing,
processors = 1, model = linearModel, seed = 1111)
# get predictive performance estimates and produce a performance plot
pacc <- predictive.acc(rfout)
# }
# NOT RUN {
# }
```

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