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(This article was first published on <strong><a href="http://smarterpoland.pl/index.php/2017/12/explain-explain-explain/"> SmarterPoland.pl » English</a></strong>, and kindly contributed to <a rel="nofollow" href="https://www.r-bloggers.com/">R-bloggers)</a> </div>
<p><img align="right" src="https://i0.wp.com/smarterpoland.pl/wp-content/uploads/2017/12/Screen-Shot-2017-12-03-at-02.47.04-300x190.png?w=350" data-recalc-dims="1"><br>
Predictive modeling is fun. With random forest, xgboost, lightgbm and other elastic models…<br>
Problems start when someone is asking how predictions are calculated.<br>
Well, some black boxes are hard to explain.<br>
And this is why we need good explainers.</p>
<p>In the June Aleksandra Paluszynska defended her master thesis <a rel="nofollow" href="https://rawgit.com/geneticsMiNIng/BlackBoxOpener/master/randomForestExplainer_Master_thesis.pdf" target="_blank">Structure mining and knowledge extraction from random forest</a>. Find the corresponding package <a rel="nofollow" href="https://mi2datalab.github.io/randomForestExplainer/" target="_blank">randomForestExplainer</a> and its vignette <a rel="nofollow" href="https://rawgit.com/MI2DataLab/randomForestExplainer/master/inst/doc/randomForestExplainer.html" target="_blank">here</a>.</p>
<p>In the September David Foster published a very interesting package <a rel="nofollow" href="https://github.com/AppliedDataSciencePartners/xgboostExplainer" target="_blank">xgboostExplainer</a>. Try it to extract useful information from a xgboost model and create waterfall plots that explain variable contributions in predictions. Read more about <a rel="nofollow" href="https://medium.com/applied-data-science/new-r-package-the-xgboost-explainer-51dd7d1aa211" target="_blank">this package here</a>.</p>
<p>In the October Albert Cheng published <a rel="nofollow" href="https://github.com/lantanacamara/lightgbmExplainer" target="_blank">lightgbmExplainer</a>. Package with waterfall plots implemented for lightGBM models. Its usage is very similar to the xgboostExplainer package.</p>
<p>Waterfall plots that explain single predictions are great. They are useful also for linear models. So if you are working with lm() or glm() try the brand new <a rel="nofollow" href="https://pbiecek.github.io/breakDown/" target="_blank">breakDown</a> package (hmm, maybe it should be named glmExplainer). It creates graphical explanations for predictions and has such a nice cheatsheet:</p>
<p><a rel="nofollow" href="https://i0.wp.com/smarterpoland.pl/wp-content/uploads/2017/12/breakDownCheatsheet.png" target="_blank"><img alt="breakDownCheatsheet" src="https://i1.wp.com/smarterpoland.pl/wp-content/uploads/2017/12/breakDownCheatsheet-1024x787.png?w=100%25" data-recalc-dims="1"></a></p>
<p>Install the package from <a rel="nofollow" href="https://pbiecek.github.io/breakDown/" target="_blank">https://pbiecek.github.io/breakDown/</a>.</p>
<p>Thanks to RStudio for the cheatsheet’s template.</p>
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