`modelDown`

generates a website with HTML summaries for predictive models. Is uses DALEX explainers to compute and plot summaries of how given models behave. We can see how exactly scores for predictions were calculated (Prediction BreakDown), how much each variable contributes to predictions (Variable Response), which variables are the most important for a given model (Variable Importance) and how well out models behave (Model Performance).

`pkgdown`

documentation: https://mi2datalab.github.io/modelDown/

An example website for regression models: https://mi2datalab.github.io/modelDown_example/

Index page presents basic information about data provided in explainers. You can also see types of all explainers given as parameters. Additionally, summary statistics are available for numerical variables. For categorical variables, tables with frequencies of factor levels are presented.

Output of function `variable_importance`

is presented in form of a plot as well as a table.

For each variable, plot is created by using function `variable_response`

. Plots can be easily navigated using links on the left side. One can provide names of variables to include in the module with argument `vr.vars`

(if argument is not used, plots for all variables of first explainer are generated).

Module presents plot generated with function `prediction_breakdown`

for particular observations. Observations to be presented can be provided by user as input parameter (named `pb.observations`

), otherwise, for each explainer, observation with highest residual value is presented. You can also see exact values of the observation in the generated table.

- Browse source code at

https://github.com/MI2DataLab/modelDown - Report a bug at

https://github.com/MI2DataLab/modelDown/issues

- Magda Tatarynowicz

Author - Kamil Romaszko

Author, maintainer - Mateusz Urbański

Author