`live`

package use case: wine quality dataThe `live`

package approximates black box model (here SVM model) with a simpler white box model (here linear regression model) to explain the local structure of a black box model and in consequence to assess how features contribute to a single prediction.

The wine quality data is a well-known dataset which is commonly used as an example in predictive modeling. The main objective associated with this dataset is to predict the quality of some variants of Portuguese ,,Vinho Verde’’ based on 11 chemical properties. According to the results from the original article, the Support Vector Machine (SVM) model performs better than other models including linear regression, neural networks and others. We will explain the prediction for the fifth case in the dataset.

```
## # A tibble: 1 x 12
## fixed_acidity volatile_acidity citric_acid residual_sugar chlorides
## <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 7.4 0.7 0 1.9 0.076
## # … with 7 more variables: free_sulfur_dioxide <dbl>,
## # total_sulfur_dioxide <dbl>, density <dbl>, pH <dbl>, sulphates <dbl>,
## # alcohol <dbl>, quality <int>
```

Once we have the fitted model, we need to generate artificial observations around the selected observation for local exploration. We use `sample_locally`

function.

```
wine_svm <- e1071::svm(quality ~., data = wine)
similar <- sample_locally(data = wine,
explained_instance = wine[5, ],
explained_var = "quality",
size = 500)
```

This function generates observations that are similar to the observation of interest. Their number is controled by `size`

argument. Two method of sampling are available through `method`

argument. Method “live” changes a value of one variable per new observations, method “permute” permutes each column. Object returned by the function is a list that contains the dataset, name of the target variable and the explained instance.

The next step is adding black box model predictions to the similar observations.

If multiple models are to be explained, there is no need to generate multiple *artificial* datasets. Predictions of each model on a single simulated dataset can be added with the use of `add_predictions`

function. A different object should be created for each model, but the same result of a call to `sample_locally`

function should be used a `to_explain`

argument. Black box model can be passed as a model object or as a name of learner. While the object created by `sample_locally`

function stores the dataset, explained instance and the name of the response variable and some additional metadata, object returned by `add_predictions`

function also stores the fitted black box model. The result of applying `sample_locally`

functions doesn’t contain the response and the result of `add_predictions`

contains a column with model predictions, which has the same name as response in original dataset. If a `mlr`

name of a black box model is passed, dataset to train the model needs to be provided, too.

Once the artificial data points around the case of interest are generated, we may fit the white box model to them. Here we fit linear regression model using `fit_explanation`

function.

```
if(require('RWeka')) {
wine_expl <- fit_explanation(live_object = similar1,
white_box = "regr.lm")
}
```

This function returns a native `mlr`

object. Model object (for example lm object) can be extracted with the use of `getLearnerModel`

function from `mlr`

. For datasets with larger number of variables, we could obtain sparse results by setting `selection = TRUE`

in the `fit_explanation`

function. With this option variable selection based on LASSO implemented in `glmnet`

package is performed. Features can be standardize before fitting explanation model by setting `standardize`

argument to `TRUE`

. When using Generalized Linear Model as a white box model it is possible to set `family`

argument to one of the distribution families available in `glm`

and `glmnet`

functions via `response_family`

argument to `fit_explanation`

. Moreover, for explanation models that support weights, observation can be weighted according to their distance from the explained instance. This behavior is controlled via `kernel`

argument. Default kernel is gaussian kernel. If observations do not need to be weighted, `identity_kernel`

can be used. User can define his own kernels, which are simply a function of two arguments (explained instance and simulated instance) that return a single number.

The white box model `wine_expl`

approximates the black box model `wine_svm`

around the selected observation. Now we can visualize it with the generic `plot`

function. If the model chosen as the white box has a `plot`

method, the function will call it on the appropriate object. Two special plots are available for regression problems.

*Forest plot* from `forestmodel`

package focuses on the local structure of the model by plotting estimates of the regressions coefficients along with confidence intervals and p-values.

*Break Down Plots* or waterfall plots from `breakDown`

package display how predictors contribute to the prediction. For details see `breakDown`

documentation.