explain.default {DALEX2} | R Documentation |
Black-box models may have very different structures. This function creates a unified representation of a model, which can be further processed by various explainers.
explain.default(model, data = NULL, y = NULL, predict_function = yhat, link = I, ..., label = tail(class(model), 1)) explain(model, data = NULL, y = NULL, predict_function = yhat, link = I, ..., label = tail(class(model), 1))
model |
object - a model to be explained |
data |
data.frame or matrix - data that was used for fitting. If not provided then will be extracted from the model |
y |
numeric vector with outputs / scores. Currently used only by |
predict_function |
function that takes two arguments: model and new data and returns numeric vector with predictions |
link |
function - a transformation/link function that shall be applied to raw model predictions |
... |
other parameters |
label |
character - the name of the model. By default it's extracted from the 'class' attribute of the model |
Please NOTE, that the model
is actually the only required argument.
But some explainers may require that others will be provided too.
An object of the class 'explainer'.
It's a list with following fields:
model
the explained model
data
the dataset used for training
predict_function
function that may be used for model predictions, shall return a single numerical value for each observation.
class
class/classes of a model
label
label, by default it's the last value from the class
vector, but may be set to any character.
apartments_lm <- lm(m2.price ~ ., data = apartments) apartments_lm_ex <- explain(apartments_lm, data = apartments, label = "apartments_lm") apartments_lm_ex ## Not run: library("breakDown2") wine_lm_model4 <- lm(quality ~ pH + residual.sugar + sulphates + alcohol, data = wine) wine_lm_explainer4 <- explain(wine_lm_model4, data = wine, label = "model_4v") wine_lm_explainer4 library("randomForest") wine_rf_model4 <- randomForest(quality ~ pH + residual.sugar + sulphates + alcohol, data = wine) wine_rf_explainer4 <- explain(wine_rf_model4, data = wine, label = "model_rf") wine_rf_explainer4 ## End(Not run)