3 ms·

also the model is analyzable so as to determine the variables which are contributing the most.

• codewithcheese 9y ago
  You might be interested in this approach to explaining the predictions for any classifier. https://github.com/marcotcr/lime https://github.com/marcotcr/lime
• nerdponx 9y ago
  For some very nice Random Forest visualizations, check out the R package "forestFloor" [0]. I also once started implementing a R package for "partial dependence plots" [1][2], which are popularly associated with Random Forests but aren't specific to them. [0]: https://CRAN.R-project.org/package=forestFloor https://CRAN.R-project.org/package=forestFloor [1]: http://scikit-learn.org/stable/auto_examples/ensemble/plot_partial_dependence.html http://scikit-learn.org/stable/auto_examples/ensemble/plot_p... [2]: https://github.com/gwerbin/statsplots/blob/master/R/partialplot.R https://github.com/gwerbin/statsplots/blob/master/R/partialp...