What’s new in DALEX v 0.4.9?

Few days ago a new version of DALEX was accepted by CRAN (v 0.4.9). Here you will find short overview what was added/changed.

DALEX is an R package with methods for visual explanation and exploration of predictive models.
Here you will find short overview with examples based on Titanic data.
For real world use cases:
Here you will find a conference talk related to credit scoring based on FICO data.
Here you will find an example use case for insurance pricing.

Major changes in the last version

Verbose model wrapping

Function explain() is now more verbose. During the model wrapping it checks the consistency of arguments. This works as unit tests for a model. This way most common problems with model exploration can be identified early during the exploration.

Support for new families of models

We have added more convenient support for gbm models. The ntrees argument for predict_function is guessed from the model structure.
Support for mlr, scikit-learn, h2o and mljar was moved to DALEXtra in order to limit number of dependencies.

Integration with other packages

DALEX has now better integration with the auditor package. DALEX explainers can be used with any function form the auditor package. So, now you can easily create an ROC plot, LIFT chart or perform analysis of residuals. This way we have access to a large number of loss functions.

Richer explainers

Explainers have now new elements. Explainers store information about packages that were used for model development along with their versions.
Latest version of explainers stores also sampling weights for the data argument.

A bit of philosophy

Cross-comparisons of models is tricky because predictive models may have very different structures and interfaces. DALEX is based on an idea of an universal adapter that can transform any model into a wrapper with unified interface that can be digest by any model agnostic tools.

In this medium article you will find a longer overview of this philosophy.

dime: Deep Interactive Model Explanations

Hubert Baniecki created an awesome package dime for serverless HTML interactive model exploration. The experimental version is at Github, here is the pkgdown website. It is a part of the DrWhy.AI project.

How does it work?

With the DALEX package you can create local and global model explanations for machine learning models. Each explanation can be visualized with a genetic plot() function.
Hubert created a generic plotD3() function which turns each explanation into an interactive D3 plot (with the help of r2d3 package). With the dime package you can combine few interactive explanations into a single dashboard. And the dashboard is serverless, you can host it at github or anywhere.

For example, the gif below shows how to combine a break down plot (local feature attribution) with ceteris paribus profiles (detailed analysis of a single variable). You can click a variable of interest to activate an appropriate ceteris paribus profile (click to play).

With the dime package you can combine any number of interactive widgets into a single dashboard. You can connect local, global explanations or EDA tools like histograms or barplots.

It’s very easy to generate such website. Just create an explainer and call the modelStudio() function.

Find examples and R codes here: https://github.com/ModelOriented/dime/blob/master/README.md

The dime package is still in the experimental phase. Your feedback is welcomed. Feel free to submit an issue with comments or ideas.

Learn about XAI in R with ,,Predictive Models: Explore, Explain, and Debug”

XAI (eXplainable artificial intelligence) is a fast growing and super interesting area.
Working with complex models generates lots of problems with model validation (on test data performance is great but drops at production), model bias, lack of stability and many others. We need more than just local explanations for predictive models.

The more complex are models the better tools are needed to understand how models are working, explore model behaviour and debug potential errors.

Two years ago I’ve initiated work on the DALEX package. Library packed with functions for local and global model exploration.
Over the time the package went through few architectural changes and now it is part of a larger universe of tools for model exploration developed at MI2DataLab with an increasing support of external contributors (join us).

To explain our philosophy behind the model exploration we (together with Tomasz Burzykowski from Hasselt) started a book ,,Predictive Models: Explore, Explain, and Debug’’.

First part, devoted to local exploration, is ready to read. It explains how to use DALEX with iBreakDown and ingredients packages for instance level explanations.
Later we will describe other packages from our universe.

Find the book-down version of here.

Find a one-page-cheatsheet here.

Let us improve these descriptions by adding pull requests or issues at the GitHub repo.
One day there will be a paper version 😉

modelDown is now on CRAN!


The modelDown package turns classification or regression models into HTML static websites.
With one command you can convert one or more models into a website with visual and tabular model summaries. Summaries like model performance, feature importance, single feature response profiles and basic model audits.

The modelDown uses DALEX explainers. So it’s model agnostic (feel free to combine random forest with glm), easy to extend and parameterise.

Here you can browse an example website automatically created for 4 classification models (random forest, gradient boosting, support vector machines, k-nearest neighbours). The R code beyond this example is here.

Fun facts:

archivist hooks are generated for every documented object. So you can easily extract R objects from the HTML website. Try

archivist::aread("MI2DataLab/modelDown_example/docs/repository/574defd6a96ecf7e5a4026699971b1d7")

– session info is automatically recorded. So you can check version of packages available at model development (https://github.com/MI2DataLab/modelDown_example/blob/master/docs/session_info/session_info.txt)

– This package is initially created by Magda Tatarynowicz, Kamil Romaszko, Mateusz Urbański from Warsaw University of Technology as a student project.

xaibot – conversations with predictive models!


If you could talk to a predictive machine learning model, what would you ask for?

Try! Michał Kuźba is developing a mind-blowing project – xai chat-bot. Dialog based system that helps to explore and understand predictive models through natural language conversations (type, speak or phone the model 😉 ).

For example, imagine that you have a random forest model that predicts survival for titanic data. With xai-bot you can chat about your chances of survival, variables that influence survival, options that you have to increase your odds or just chat about life models.


The chatbot is based on dialog-flow google infrastructure. It communicates with DALEX explainers written in R through plumber REST API.

Find the chatbot here: https://kmichael08.github.io.

The project is under development, but the bot is already pretty smart.

So, have fun!

How to design a model visualisation @ Gdansk satRdays


I had amazing weekend in Gdansk thanks to the satRday conference organized by Olgun Aydin, Ania Rybinska and Michal Maj.

Together with Hanna Piotrowska we had a talk ,,Machine learning meets design. Design meets machine learning”. Hanna redesigned DALEX visualisations (DALEX is a set of tools for visual explanation of predictive ML models). During the talk she explained what and why was changed.

See for example the metamorphosis of the Break Down explainer. How many differences can you spot?

Every change (axis, reading order, spacing, colors, descriptions, background, annotations) serves some purpose.

Find our presentation at slideshare.

List of satRday talks (machine learning was quite popular).

Hanna design is implemented in ggplot2 thanks to Tomasz Mikołajczyk and in D3 thanks to Huber Baniecki! Find more examples of how to use new plots here.

Make it explainable!

Most people make the mistake of thinking design is what it looks like… People think it’s this veneer — that the designers are handed this box and told, ‚Make it look good!’ That’s not what we think design is. It’s not just what it looks like and feels like. Design is how it works.

Steve Jobs, The New York Times, 2003.

Same goes with interpretable machine learning.
Recently, I am talking a lot about interpretations and explainability. And sometimes I got impression that techniques like SHAP, Break Down, LIME, SAFE are treated like magical incantations that converts complex predictive models into ,,something interpretable’’.

But interpretability/explainability is not a binary feature that you have it or not. It’s a process. The goal is to increase our understanding of the model behavior. Try different techniques to broaden the knowledge about the model or about model predictions.
Maybe you will never explain 100%, but you will understand more.

XAI/IML (eXplainable Artificial Intelligence/Interpretable Machine Learning) techniques can be used not only for post-hoc explainability, but also for model maintenance, debugging or in early phases of crisp modeling. Visual tools like PDP/ALE/CeterisParibus will change the way how we approach modeling and how we interact with models. We as model developers, model auditors or users.

Together with Tomasz Burzykowski from UHasselt we work on a book about the methodology for visual exploration, explanation and debugging predictive models.

Find the early version here https://pbiecek.github.io/PM_VEE/.

There is a lot of R snippets that shows how to use DALEX (and sometimes other packages like shapper, ingredients, iml, iBreakDown, condvis, localModel, pdp) to better understand some aspects of your predictive model.

It’s a work in process and even in an early dirty phase (despite the fact that we have started a year ago).
Feel free to comment it, or suggest improvements. Easiest way to do this is to add a new issue.

Code snippets are fully thanks to archivist hooks. I think that it’s a first book that uses archivist hooks for blended experience. You can read about a model online and in just one line of code you can download an object to your R console.

First chapters show how to use Ceteris Paribus Profiles / Individual Conditional Expectations to perform what-if/sensitivity analysis of a model.

DALEX for keras and parsnip

DALEX is a set of tools for explanation, exploration and debugging of predictive models. The nice thing about it is that it can be easily connected to different model factories.

Recently Michal Maj wrote a nice vignette how to use DALEX with models created in keras (an open-source neural-network library in python with an R interface created by RStudio). Find the vignette here.
Michal compared a keras model against deeplearning from h2o package, so you can check which model won on the Titanic dataset.

Next nice vignette was created by Szymon Maksymiuk. In this vignette Szymon shows how to use DALEX with parsnip models (parsnip is a part of the tidymodels ecosystem, created by Max Kuhn and Davis Vaughan). Models like boost_tree, mlp and svm_rbf are competing on the Titanic data.

These two new vignettes add to our collection how to use DALEX with mlr, caret, h2o and others model factories.

iBreakDown: faster, prettier and more precise explanations for predictive models (with interactions)

LIME and SHAP are two very popular methods for instance level explanations of machine learning models (XAI).
They work nicely for images and text inputs, but share similar weakness in case of tabular data: explanations are additive while complex models are (sometimes) not. iBreakDown addresses this problem.

iBreakDown is a a successor of the breakDown package. Yesterday it has arrived on CRAN. Key new features are:

– It identifies and shows feature interactions (if there are local interactions in the model).
– It is much faster. For additive explanations the complexity is O(p) instead of O(p^2).
– The plotD3 function creates an interactive D3-based break-down plot (thanks to r2d3).
– iBreakDown has a new design, created by Hanna Dyrcz. We will have a talk about it ,,Machine learning meets design. Design meets machine learning.” at satRdays. Try the new theme theme_drwhy()!.
– It shows explanation level uncertainty – how good are explanations?

A methodology behind this package is described in the iBreakDown: Uncertainty of Model Explanations for Non-additive Predictive Models.

A nice titanic-powered use-case is described in the titanic vignette.

An example of the D3 interactive explainer is here.

Some intuition is introduced in the Visual Exploration, Explanation and Debugging (working version, still in progress).

iBreakDown is a part of the DrWhy.AI family of explainers consistent with the DALEX.

Let us know if you like it. Feel free to create a pull request with new features, add issue with new idea or star the github repository if you like this package.

Bank będzie musiał wyjaśnić… czyli o wyjaśnialnych modelach predykcyjnych

Czym są wyjaśnialne modele predykcyjne?

Interpretowalne uczenie maszynowe (IML od Interpretable Machine Learning) czy wyjaśnialna syntetyczna inteligencja (XAI od eXplainable Artificial Intelligence) to względnie nowa, a ostatnio bardzo szybko rozwijająca się, gałąź uczenia maszynowego.

W skrócie chodzi o to, by konstruować takie modele, dla których człowiek możne zrozumieć skąd biorą się decyzje modelu. Złożone modele typu lasy losowe czy głębokie sieci są ok, o ile potrafimy w jakiś sposób wyjaśnić co wpłynęło na konkretną decyzję modelu.

Po co?

W ostatnich latach często uczenie maszynowe było uprawiane ,,w stylu Kaggle”. Jedynym kryterium oceny modelu była skuteczność modelu na jakimś ustalonym zbiorze testowym. Takie postawienie sprawie często zamienia się w bezsensowne żyłowanie ostatnich 0.00001% accuracy na zbiorze testowym.

Tak wyżyłowane modele najczęściej epicko upadają w zderzeniu z rzeczywistością. Ja na prezentacjach lubię wymieniać przykłady Google Flu, Watson for Oncology, Amazon CV, COMPAS i recydywizm czy przykłady z książki ,,Broń matematycznej zagłady”. Ale lista jest znacznie dłuższa.

Dlaczego to takie ważne?

W lutym fundacja Panoptykon pisała Koniec z „czarną skrzynką” przy udzielaniu kredytów. W ostatni czwartek (21 marca) w gazecie Bankier można było znaleźć ciekawy artykuł Bank będzie musiał wyjaśnić, dlaczego odmówił kredytu, w której opisuje niektóre konsekwencje ustawy przyjętej przez Senat.

Przykładowy cytat:
,,Ustawa wprowadza także m.in. przepis nakazujący bankom przedstawienie klientowi wyjaśnienia dotyczącego tego, które dane osobowe miały wpływ na ostatecznie dokonaną ocenę zdolności kredytowej. Obowiązek ten będzie dotyczył zarówno sytuacji, w której decyzja ta została podjęta w pełni zautomatyzowanym procesie, na podstawie tzw. algorytmów, jak i sytuacji, w której w podejmowaniu decyzji brał udział także człowiek”.

Wygląda więc na to, że niedługo wyjaśnialne uczenie maszynowe spotka nas w okienkach bankowych przy okazji decyzji kredytowych.

Nie tylko banki

Okazuje się, że temat wyjaśnialności w czwartek omawiany był nie tylko w Senacie. Akurat byłem tego dnia na bardzo ciekawej konferencji Polish Business Analytics Summit, na której dr Andrey Sharapov opowiadał o tym jak Lidl wykorzystuje techniki XAI i IML do lepszego wspomagania decyzji.

Zbudować model jest prosto, ale pokazać wyniki modelu biznesowi, tak by ten wiedział jak na ich podstawie podejmować lepsze decyzje – to jest wyzwanie dla XAI. Andrey Sharapov prowadzi na LinkedIn ciekawą grupę na którą wrzuca materiały o wyjaśnialnym uczeniu maszynowym. Sporo pozycji można też naleźć na tej liście.

Na poniższym zdjęciu jest akurat przykład wykorzystania techniki Break Down (made in MI2 Data Lab!!!) do wspomagania decyzji dotyczących kampanii marketingowych.

Warszawa po raz trzeci

Aż trudno uwierzyć w ten zbieg okoliczności, ale tego samego dnia (tak, wciąż piszę o 21 marca) na Spotkaniach Entuzjastów R profesor Marco Robnik omawiał różne techniki wyjaśnialności opartej o permutacje.

Skupił się na technika EXPLAIN i IME, ale było też o LIME i SHAP a na niektórych slajdach pojawiał się nasz DALEX i live (choć pewnie my byśmy już reklamowani nowsze rozwiązanie Mateusza Staniaka, czyli pakiet localModels).

Btw, spotkanie było nagrywane, więc niedługo powinno być dostępne na youtube.

Gdzie mogę dowiedzieć się więcej?

Wyjaśnialne uczenie maszynowe to przedmiot badań znacznej części osób z MI2DataLab. Rozwijamy platformę do automatycznej analizy, eksploracji i wyjaśnień dla modeli predykcyjnych DrWhy.AI.

Niedługo napisze więcej o materiałach i okazjach podczas których można dowiedzieć się więcej o ciekawych zastosowaniach technik wyjaśnialnego uczenia maszynowego w finansach, medycynie spersonalizowanej czy innych ciekawych miejscach.