To illustrate applications of *auditor* to regression problems we will use an artificial dataset dragons available in the *DALEX* package. Our goal is to predict the length of life of dragons.

```
library(DALEX)
data("dragons")
head(dragons)
```

```
## year_of_birth height weight scars colour year_of_discovery
## 1 -1291 59.40365 15.32391 7 red 1700
## 2 1589 46.21374 11.80819 5 red 1700
## 3 1528 49.17233 13.34482 6 red 1700
## 4 1645 48.29177 13.27427 5 green 1700
## 5 -8 49.99679 13.08757 1 red 1700
## 6 915 45.40876 11.48717 2 red 1700
## number_of_lost_teeth life_length
## 1 25 1368.4331
## 2 28 1377.0474
## 3 38 1603.9632
## 4 33 1434.4222
## 5 18 985.4905
## 6 20 969.5682
```

```
lm_model <- lm(life_length ~ ., data = dragons)
```

```
library("randomForest")
set.seed(59)
rf_model <- randomForest(life_length ~ ., data = dragons)
```

The beginning of each analysis is creation of an `explainer`

object with DALEX package. It’s an object that can be used to audit a model.

```
lm_exp <- DALEX::explain(lm_model, label = "lm", data = dragons, y = dragons$life_length)
```

```
## Preparation of a new explainer is initiated
## -> model label : lm
## -> data : 2000 rows 8 cols
## -> target variable : 2000 values
## -> predict function : yhat.lm will be used ([33mdefault[39m)
## -> predicted values : numerical, min = 540.9447 , mean = 1370.986 , max = 3925.691
## -> residual function : difference between y and yhat ([33mdefault[39m)
## -> residuals : numerical, min = -108.2062 , mean = -3.701928e-12 , max = 113.8603
## [32mA new explainer has been created![39m
```

```
rf_exp <- DALEX::explain(rf_model, label = "rf", data = dragons, y = dragons$life_length)
```

```
## Preparation of a new explainer is initiated
## -> model label : rf
## -> data : 2000 rows 8 cols
## -> target variable : 2000 values
## -> predict function : yhat.randomForest will be used ([33mdefault[39m)
## -> predicted values : numerical, min = 610.9752 , mean = 1370.181 , max = 3292.296
## -> residual function : difference between y and yhat ([33mdefault[39m)
## -> residuals : numerical, min = -135.4756 , mean = 0.8047108 , max = 720.0888
## [32mA new explainer has been created![39m
```

In this section we give short overview of a visual validation of model errors and show the propositions for the validation scores. Auditor helps to find answers for questions that may be crucial for further analyses.

Does the model fit data? Is it not missing the information?

Which model has better performance?

How similar models are?

In further sections, we overview auditor functions for analysis of model residuals. They are discussed in alphabetical order.

First, we need to create a `auditor_model_residual`

objects.

```
library(auditor)
lm_cd <- model_cooksdistance(lm_exp)
```

Some plots may require specified variable or fitted values for `modelResidual`

object.

Cook's distance is used to estimate of the influence of an single observation. It is a tool for identifying observations that may negatively affect the model.

Data points indicated by Cook's distances are worth checking for validity. Cook's distances may be also used for indicating regions of the design space where it would be good to obtain more observations.

Cook’s Distances are calculated by removing the i-th observation from the data and recalculating the model. It shows how much all the values in the model change when the i-th observation is removed.

In the case of models of classes other than `lm`

and `glm`

the distances are computed directly from the definition, so this may take a while.
In this example we will compute them for a linear model.

```
plot(lm_cd)
```

Other methods and plots are described in vignettes: