Welcome to the second part of the forester blog. In the previous part, we explained the main idea of the forester package, the motivations behind it, its advantages, and the innovations it brings to the ML world. You should definitely check it out! In this part, however, we will focus on showing the wide range of possibilities of the forester package and things you can achieve with it. We will present you the main functions of the package with their parameters and show how you can use them in your problems.

We discussed each step in the previous part using the graph below. Now we will try to explain how our package exactly works and what happens between the first and the last step of the process. The basic scheme of functions of our package is presented on the graph below. The only thing user has to do to create a model is to run one function. Then the data preprocessing is performed.

Predictive modeling is invaded by elastic, yet complex methods such as neural networks or ensembles (model stacking, boosting or bagging). Such methods are usually described by a large number of parameters or hyper parameters - a price that one needs to pay for elasticity. The very number of parameters makes models hard to understand. This paper describes a consistent collection of explainers for predictive models, a.k.a. black boxes. Each explainer is a technique for exploration of a black box model. Presented approaches are model-agnostic, what means that they extract useful information from any predictive method despite its internal structure. Each explainer is linked with a specific aspect of a model. Some are useful in decomposing predictions, some serve better in understanding performance, while others are useful in understanding importance and conditional responses of a particular variable. Every explainer presented in this paper works for a single model or for a collection of models. In the latter case, models can be compared against each other. Such comparison helps to find strengths and weaknesses of different approaches and gives additional possibilities for model validation. Presented explainers are implemented in the DALEX package for R. They are based on a uniform standardized grammar of model exploration which may be easily extended. The current implementation supports the most popular frameworks for classification and regression.