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...continue reading.
Category: English
Tell Me a Story: How to Generate Textual Explanations for Predictive Models
TL;DR: If you are going to explain predictions for a black box model you should combine statistical charts with natural language descriptions. This combination is more powerful than SHAP/LIME/PDP/Break Down...continue reading.
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...continue reading.
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...continue reading.
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...continue reading.
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...continue reading.
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...continue reading.
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...continue reading.
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....continue reading.
Explore the landscape of R packages for automated data exploration
Do you spend a lot of time on data exploration? If yes, then you will like today’s post about AutoEDA written by Mateusz Staniak. If you ever dreamt of automating...continue reading.
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...continue reading.
DALEX has a new skin! Learn how it was designed at gdansk2019.satRdays
DALEX is an R package for visual explanation, exploration, diagnostic and debugging of predictive ML models (aka XAI – eXplainable Artificial Intelligence). It has a bunch of visual explainers for...continue reading.
shapper is on CRAN, it’s an R wrapper over SHAP explainer for black-box models
Written by: Alicja Gosiewska In applied machine learning, there are opinions that we need to choose between interpretability and accuracy. However in field of the Interpretable Machine Learning, there are...continue reading.
x-mas tRees with gganimate, ggplot, plotly and friends
At the last homework before Christmas I asked my students from DataVisTechniques to create a ,,Christmas style” data visualization in R or Python (based on simulated data). Libaries like rbokeh,...continue reading.
Data, movies and ggplot2
Facebook Twitter Google+ LinkedIn Yet another boring barplot? No! I’ve asked my students from MiNI WUT to visualize some data about their favorite movies or series. Results are pretty awesome....continue reading.
Break Down: model explanations with interactions and DALEX in the BayArea
Facebook Twitter Google+ LinkedIn The breakDown package explains predictions from black-box models, such as random forest, xgboost, svm or neural networks (it works for lm and glm as well). As...continue reading.
