Publications by smarterpoland
Ceteris Paribus Plots – a new DALEX companion
If you like magical incantations in Data Science, please welcome the Ceteris Paribus Plots. Otherwise feel free to call them What-If Plots. Ceteris Paribus (latin for all else unchanged) Plots explain complex Machine Learning models around a single observation. They supplement tools like breakDown, Shapley values, LIME or LIVE. In addition to fea...
1815 sym 6 img
Not only LIME
I’ve heard about a number of consulting companies, that decided to use simple linear model instead of a black box model with higher performance, because ,,client wants to understand factors that drive the prediction’’. And usually the discussion goes as following: ,,We have tried LIME for our black-box model, it is great, but it is not work...
1109 sym 2 img
modelDown: a website generator for your predictive models
I love the pkgdown package. With a single line of code you can create a complete website with examples, vignettes and documentation for your package. Brilliant! So what about a website generator for predictive models? Imagine that you can take a set of predictive models (generated with caret, mlr, glm, xgboost or randomForest, anything) and autom...
5503 sym R (333 sym/3 pcs) 12 img
Local Goodness-of-Fit Plots / Wangkardu Explanations – a new DALEX companion
The next DALEX workshop will take place in 4 days at UseR. In the meantime I am working on a new explainer for a single observation. Something like a diagnostic plot for a single observation. Something that extends Ceteris Paribus Plots. Something similar to Individual Conditional Expectation (ICE) Plots. An experimental version is implemented i...
2832 sym 10 img
No worries! Afterthoughts from UseR 2018
This year the UseR conference took place in Brisbane, Australia. UseR is my favorite conference and this one was mine 11th (counting from Dortmund 2008). Every UseR is unique. Every UseR is great. But my feelings are that European UseRs are (on average) more about math, statistics and methodology while US UseRs are more about big data, data sci...
3288 sym 6 img
Ceteris Paribus v0.3 is on CRAN
Ceteris Paribus package is a part of DALEX family of model explainers. Version 0.3 just gets to CRAN. It’s equipped with new functions for very elastic visual exploration of black box models. Its grammar generalizes Partial Dependency Plots, Individual Conditional Expectations, Wangkardu Plots and gives a lot of flexibility in model comparisons...
1160 sym 4 img
Break Down: model explanations with interactions and DALEX in the BayArea
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 a result you gets decomposition of model prediction that can be attributed to particular variables. The version 0.3 has a new function break_down. It identifies pairwise interactions of va...
1666 sym R (875 sym/1 pcs) 4 img
Data, movies and ggplot2
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. Believe me or not, but charts in these posters are created with ggplot2 (most of them)! Star Wars Fan of StaR WaRs? Find out which color is the most popular for lightsabers! Yes, these li...
2257 sym 16 img
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, ggiraph, vegalite, shiny+ggplot2 or plotly were popular last year. This year there are also some nice submissions that use gganimate. Find source code...
1080 sym 37 img
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 more and more new ideas for explaining black-box models. One of the best known method for local explanations is SHapley Additive exPlanations (...
4980 sym R (3551 sym/14 pcs) 3 img