About the data

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.

wine[5, ]

## # A tibble: 1 x 12
##   fixed_acidity volatile_acidity citric_acid residual_sugar chlorides
##           <dbl>            <dbl>       <dbl>          <dbl>     <dbl>
## 1          7.40            0.700          0.           1.90    0.0760
## # ... with 7 more variables: free_sulfur_dioxide <dbl>,
## #   total_sulfur_dioxide <dbl>, density <dbl>, pH <dbl>, sulphates <dbl>,
## #   alcohol <dbl>, quality <int>

Explaining the prediction

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. Optionally, this dataset can be standardized. Object returned by the function is a list that contains the dataset and the name of target variable.

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

similar1 <- add_predictions(data = wine,
                           to_explain = similar,
                           black_box_model = wine_svm)

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 and the name of the response variable, 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.

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.

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. 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.

The white box model wine_expl approximates the black box model wine_svm around the selected observation. Now we can visualize it with the plot_explanation 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.

plot_explanation(model = wine_expl, regr_plot_type = "forestplot",
                 explained_instance = wine[5, ])

Break Down Plots or waterfall plots from breakDown package display how predictors contribute to the prediction. For details see breakDown documentation.

plot_explanation(model = wine_expl, regr_plot_type = "waterfallplot",
                 explained_instance = wine[5, ])