---
title: "Explaining classification models with localModel package"
author: "Mateusz Staniak"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Explaining classification models with localModel package}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r setup, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  warning = FALSE,
  message = FALSE,
  error = FALSE
)
```

Explaining classification models with the [`localModel`](https://github.com/ModelOriented/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`](https://github.com/ModelOriented/DALEX2) package.
As in the regression example from _Introduction to the localModel package_, we will first create a random forest model and a [`DALEX2`](https://github.com/ModelOriented/DALEX2) explainer.
Details about the method can be found in the _Methodology behind localModel package_ vignette.

```{r model}
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`](https://github.com/ModelOriented/DALEX2), we have built-in predict functions for some types of models. Random Forest is among these models.

```{r explanation}
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.