# Other types of models

In the following, we explain the `counterfactuals` workflow for both a classification and a regression task using concrete use cases.

    library("counterfactuals")
    library("iml")
    library("rpart")

# Other types of models

The `Predictor` class of the `iml` package provides the necessary flexibility to cover classification and regression models fitted with diverse R packages. In the introduction vignette, we saw models fitted with the `mlr3` and `randomForest` packages. In the following, we show extensions to - an classification tree fitted with the `caret` package, the `mlr` (a predecesor of `mlr3`) and `tidymodels`. For each model we generate counterfactuals for the 100th row of the plasma dataset of the `gamlss.data` package using the `WhatIf` method.

    data(plasma, package = "gamlss.data")
    x_interest = plasma[100L,]

## rpart - caret package

    library("caret")
    treecaret = caret::train(retplasma ~ ., data = plasma[-100L,], method = "rpart",
      tuneGrid = data.frame(cp = 0.01))
    predcaret = Predictor$new(model = treecaret, data = plasma[-100L,], y = "retplasma")
    predcaret$predict(x_interest)
    #>   .prediction
    #> 1    342.9231
    nicecaret = NICERegr$new(predcaret, optimization = "proximity",
      margin_correct = 0.5, return_multiple = FALSE)
    nicecaret$find_counterfactuals(x_interest, desired_outcome = c(500, Inf))
    #> 1 Counterfactual(s)
    #>
    #> Desired outcome range: [500, Inf]
    #>
    #> Head:
    #>    age sex smokstat      bmi vituse calories   fat fiber alcohol cholesterol betadiet retdiet betaplasma
    #> 1:  46   1        3 35.25969      3   2667.5 131.6  10.1       0       550.5     1210    1291        218

## rpart - tidymodels package

    library("tidymodels")
    treetm = decision_tree(mode = "regression", engine = "rpart") %>%
      fit(retplasma ~ ., data = plasma[-100L,])
    predtm = Predictor$new(model = treetm, data = plasma[-100L,], y = "retplasma")
    predtm$predict(x_interest)
    #>      .pred
    #> 1 342.9231
    nicetm = NICERegr$new(predtm, optimization = "proximity",
      margin_correct = 0.5, return_multiple = FALSE)
    nicetm$find_counterfactuals(x_interest, desired_outcome = c(500, Inf))
    #> 1 Counterfactual(s)
    #>
    #> Desired outcome range: [500, Inf]
    #>
    #> Head:
    #>    age sex smokstat      bmi vituse calories   fat fiber alcohol cholesterol betadiet retdiet betaplasma
    #> 1:  46   1        3 35.25969      3   2667.5 131.6  10.1       0       550.5     1210    1291        218

## rpart - mlr package

    library("mlr")
    task = mlr::makeRegrTask(data = plasma[-100L,], target = "retplasma")
    mod = mlr::makeLearner("regr.rpart")

    treemlr = mlr::train(mod, task)
    predmlr = Predictor$new(model = treemlr, data = plasma[-100L,], y = "retplasma")
    predmlr$predict(x_interest)
    #>   .prediction
    #> 1    342.9231
    nicemlr = NICERegr$new(predmlr, optimization = "proximity",
      margin_correct = 0.5, return_multiple = FALSE)
    nicemlr$find_counterfactuals(x_interest, desired_outcome = c(500, Inf))
    #> 1 Counterfactual(s)
    #>
    #> Desired outcome range: [500, Inf]
    #>
    #> Head:
    #>    age sex smokstat      bmi vituse calories   fat fiber alcohol cholesterol betadiet retdiet betaplasma
    #> 1:  46   1        3 35.25969      3   2667.5 131.6  10.1       0       550.5     1210    1291        218

## Decision tree - rpart package

    treerpart = rpart(retplasma ~ ., data = plasma[-100L,])
    predrpart = Predictor$new(model = treerpart, data = plasma[-100L,], y = "retplasma")
    predrpart$predict(x_interest)
    #>       pred
    #> 1 342.9231
    nicerpart = NICERegr$new(predrpart, optimization = "proximity",
      margin_correct = 0.5, return_multiple = FALSE)
    nicerpart$find_counterfactuals(x_interest, desired_outcome = c(500, Inf))
    #> 1 Counterfactual(s)
    #>
    #> Desired outcome range: [500, Inf]
    #>
    #> Head:
    #>    age sex smokstat      bmi vituse calories   fat fiber alcohol cholesterol betadiet retdiet betaplasma
    #> 1:  46   1        3 35.25969      3   2667.5 131.6  10.1       0       550.5     1210    1291        218