knitr::opts_chunk$set( collapse = TRUE, comment = "#>", warning = FALSE, message = FALSE, error = FALSE )

Explaining classification models with the `localModel`

package is just as simple as explaining regression.
It is enough to work with predicted scores (class probabilities) rather than classes.
In multiclass setting, a separate explanation is provided for each class probability.

We will work with the `HR`

dataset from `DALEX2`

package.
As in the regression example from *Introduction to the localModel package*, we will first create a random forest model and a `DALEX2`

explainer.
Details about the method can be found in the *Methodology behind localModel package* vignette.

library(DALEX) library(randomForest) library(localModel) data('HR') set.seed(17) mrf <- randomForest(status ~., data = HR, ntree = 100) explainer <- explain(mrf, HR[, -6], predict_function = function(x, y) predict(x, y, type = "prob")) new_observation <- HR[10, -6] new_observation

In `DALEX2`

, we have built-in predict functions for some types of models. Random Forest is among these models.

model_lok <- individual_surrogate_model(explainer, new_observation, size = 500, seed = 17) plot(model_lok) plot(model_lok, geom = "bar")

The plot shows how predictions for different classes are influenced by different features. For the actually predicted class, hours and evaluation have a strong positive effect.

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