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(This article was first published on <strong><a href="http://smarterpoland.pl/index.php/2018/06/ceteris-paribus-plots-a-new-dalex-companion/"> English – SmarterPoland.pl</a></strong>, and kindly contributed to <a rel="nofollow" href="https://www.r-bloggers.com/">R-bloggers)</a> </div>
<p>If you like magical incantations in Data Science, please welcome the Ceteris Paribus Plots. Otherwise feel free to call them <i>What-If Plots</i>.</p>
<p>Ceteris Paribus (latin for <i>all else unchanged</i>) Plots explain complex Machine Learning models around a single observation. They supplement tools like <a rel="nofollow" href="https://cran.r-project.org/package=breakDown" target="_blank">breakDown</a>, <a rel="nofollow" href="https://cran.r-project.org/package=iml" target="_blank">Shapley values</a>, LIME or LIVE. In addition to feature importance/feature attribution, now we can see how the model response changes along a specific variable, keeping all other variables unchanged.</p>
<p>How cancer-risk-scores change with age? How credit-scores change with salary? How insurance-costs change with age? </p>
<p>Well, use the <a rel="nofollow" href="https://github.com/pbiecek/ceterisParibus" target="_blank">ceterisParibus</a> package to generate plots like the one below.<br>
Here we have an explanation for a random forest model that predicts apartments prices. Presented profiles are prepared for a single observation marked with dashed lines (130m2 apartment on 3rd floor). From these profiles one can read how the model response is linked with particular variables.</p>
<p><center><img src="https://i0.wp.com/smarterpoland.pl/wp-content/uploads/2018/06/use_case_1.png?w=450" data-recalc-dims="1"></center></p>
<p>Instead of original values on the OX scale one can plot qunatiles. This way one can put all variables in a single plot.</p>
<p><center><img src="https://i1.wp.com/smarterpoland.pl/wp-content/uploads/2018/06/use_case_3.png?w=450" data-recalc-dims="1"></center></p>
<p>And once all variables are in the same scale, one can compare two or more models.</p>
<p><center><img src="https://i0.wp.com/smarterpoland.pl/wp-content/uploads/2018/06/use_case_4.png?w=450" data-recalc-dims="1"></center></p>
<p>Yes, they are model agnostic and will work for any model!<br>
Yes, they can be interactive (see plot_interactive function or examples below)!<br>
And yes, you can use them with other DALEX explainers!<br>
<a rel="nofollow" href="https://pbiecek.github.io/ceterisParibus/articles/whatIf1d.html" target="_blank">More examples with R code.</a></p>
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