explain_mlr | R Documentation |

DALEX is designed to work with various black-box models like tree ensembles, linear models, neural networks etc. Unfortunately R packages that create such models are very inconsistent. Different tools use different interfaces to train, validate and use models. One of those tools, which is one of the most popular one is the mlr package. We would like to present dedicated explain function for it.

```
explain_mlr(
model,
data = NULL,
y = NULL,
weights = NULL,
predict_function = NULL,
predict_function_target_column = NULL,
residual_function = NULL,
...,
label = NULL,
verbose = TRUE,
precalculate = TRUE,
colorize = !isTRUE(getOption("knitr.in.progress")),
model_info = NULL,
type = NULL
)
```

`model` |
object - a model to be explained |

`data` |
data.frame or matrix - data which will be used to calculate the explanations. If not provided, then it will be extracted from the model. Data should be passed without a target column (this shall be provided as the |

`y` |
numeric vector with outputs/scores. If provided, then it shall have the same size as |

`weights` |
numeric vector with sampling weights. By default it's |

`predict_function` |
function that takes two arguments: model and new data and returns a numeric vector with predictions. By default it is |

`predict_function_target_column` |
Character or numeric containing either column name or column number in the model prediction object of the class that should be considered as positive (i.e. the class that is associated with probability 1). If NULL, the second column of the output will be taken for binary classification. For a multiclass classification setting, that parameter cause switch to binary classification mode with one vs others probabilities. |

`residual_function` |
function that takes four arguments: model, data, target vector y and predict function (optionally). It should return a numeric vector with model residuals for given data. If not provided, response residuals ( |

`...` |
other parameters |

`label` |
character - the name of the model. By default it's extracted from the 'class' attribute of the model |

`verbose` |
logical. If TRUE (default) then diagnostic messages will be printed |

`precalculate` |
logical. If TRUE (default) then |

`colorize` |
logical. If TRUE (default) then |

`model_info` |
a named list ( |

`type` |
type of a model, either |

explainer object (`explain`

) ready to work with DALEX

```
## Not run:
library("DALEXtra")
titanic_test <- read.csv(system.file("extdata", "titanic_test.csv", package = "DALEXtra"))
titanic_train <- read.csv(system.file("extdata", "titanic_train.csv", package = "DALEXtra"))
library("mlr")
task <- mlr::makeClassifTask(
id = "R",
data = titanic_train,
target = "survived"
)
learner <- mlr::makeLearner(
"classif.gbm",
par.vals = list(
distribution = "bernoulli",
n.trees = 500,
interaction.depth = 4,
n.minobsinnode = 12,
shrinkage = 0.001,
bag.fraction = 0.5,
train.fraction = 1
),
predict.type = "prob"
)
gbm <- mlr::train(learner, task)
explain_mlr(gbm, titanic_test[,1:17], titanic_test[,18])
## End(Not run)
```

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