c(person("Przemyslaw", "Biecek", email = "przemyslaw.biecek@gmail.com", role = c("aut", "cre"),
comment = c(ORCID = "0000-0001-8423-1823")),
person("Szymon", "Maksymiuk", role = "aut",
comment = c(ORCID = "0000-0002-3120-1601")),
person("Hubert", "Baniecki", role = "aut",
comment = c(ORCID = "0000-0001-6661-5364")))
ggplot2, iBreakDown (>= 1.3.1), ingredients (>= 2.0)
Przemyslaw Biecek [aut, cre] (<https://orcid.org/0000-0001-8423-1823>),
Szymon Maksymiuk [aut] (<https://orcid.org/0000-0002-3120-1601>),
Hubert Baniecki [aut] (<https://orcid.org/0000-0001-6661-5364>)

Any unverified black box model is the path to failure. Opaqueness leads to distrust.
Distrust leads to ignoration. Ignoration leads to rejection.
DALEX package xrays any model and helps to explore and explain its behaviour.
Machine Learning (ML) models are widely used and have various applications in classification
or regression. Models created with boosting, bagging, stacking or similar techniques are often
used due to their high performance. But such black-box models usually lack direct interpretability.
DALEX package contains various methods that help to understand the link between input variables
and model output. Implemented methods help to explore the model on the level of a single instance
as well as a level of the whole dataset.
All model explainers are model agnostic and can be compared across different models.
DALEX package is the cornerstone for 'DrWhy.AI' universe of packages for visual model exploration.
Find more details in (Biecek 2018) <arXiv:1806.08915>.

gower, ranger, yardstick, testthat, methods

Machine Learning models are widely used and have various applications in classification or regression tasks.
Due to increasing computational power, availability of new data sources and new methods, ML models are more and more complex.
Models created with techniques like boosting, bagging of neural networks are true black boxes.
It is hard to trace the link between input variables and model outcomes.
They are used because of high performance, but lack of interpretability is one of their weakest sides.
In many applications we need to know, understand or prove how input variables are used in the model and what impact do they have on final model prediction.
DALEX2 is a collection of tools that help to understand how complex predictive models are working.
DALEX2 is a part of DrWhy universe for tools for Explanation, Exploration and Visualisation for Predictive Models.