- 1 Introduction
- 2 Architecture of DALEX
- 3 Model understanding
- 4 Prediction understanding
- 5 Ceteris Paribus Profiles
- 5.1 Ceteris Paribus profiles for a single observation
- 5.2 Exploration of local structure with Ceteris Paribus profiles
- 5.3 Exploration of global structure with Ceteris Paribus profiles
- 5.4 What-If scenarios: Single Observation and Multiple Models
- 5.5 Exploration of multiclass classification models
- 5.6 Global Structure and Multiple Models

- 6 Epilogue
- 7 Exercises

In this chapter we introduce two groups of explainers that can be used to boost our understanding of model predictions.

- Section 4.1 presents explainers that helps to identify outliers.
- Section 4.2 presents explainers for model predictions. Each prediction can be split into parts attributed to particular variables. Having found out which variables are important and whether the prediction is accurate, one can validate the model.

Explainers presented here are illustrated based on two models fitted to the `apartments`

data.

```
library("DALEX")
apartments_lm_model <- lm(m2.price ~ construction.year + surface + floor +
no.rooms + district, data = apartments)
library("randomForest")
set.seed(59)
apartments_rf_model <- randomForest(m2.price ~ construction.year + surface + floor +
no.rooms + district, data = apartments)
```

First we need to prepare wrappers for these models. They are in `explainer_lm`

and `explainer_rf`

objects.