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 … Czytaj dalej Make it explainable!
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 … Czytaj dalej DALEX for keras and parsnip
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 the first, laborious part of data analysis when you get to know the variables, print descriptive statistics, draw a lot of histograms and scatter plots … Czytaj dalej Explore the landscape of R packages for automated data exploration
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 … Czytaj dalej iBreakDown: faster, prettier and more precise explanations for predictive models (with interactions)
W najbliższy czwartek o godzinie 18:00 startujemy z 38. spotkaniem Entuzjastów R. Aż trudno uwierzyć, że minęło już 5 lat od naszego pierwszego spotkania w ICMie. Przez te 5 lat gościliśmy ponad 70 prelegentów, często osoby, które znaliśmy z ciekawych blogów, pakietów czy książek. Większość prelegentów pracuje w Warszawie, ale też byli ciekawi goście z … Czytaj dalej To już 5 lat! Gość specjalny na najbliższym SERze opowie o wyjaśnialnym ML
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 different aspects of predictive models. Some of them are useful during model development some for fine tuning, model diagnostic or model explanations. Recently Hanna Dyrcz … Czytaj dalej DALEX has a new skin! Learn how it was designed at gdansk2019.satRdays
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 more and more new ideas for explaining black-box models. One of the best known method for local explanations is SHapley Additive exPlanations (SHAP). The SHAP … Czytaj dalej shapper is on CRAN, it’s an R wrapper over SHAP explainer for black-box models
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, ggiraph, vegalite, shiny+ggplot2 or plotly were popular last year. This year there are also some nice submissions that use gganimate. Find source codes here. Plots … Czytaj dalej x-mas tRees with gganimate, ggplot, plotly and friends