iML Package for Model Agnostic Interpretable Machine Learning

In this video the presenter goes over a new R package called ‘iML.’ This package has a lot of power when explaining global and local feature importance. These explanations are critical, especially in the health field and if your under GDPR regulations. Now, with the combination of Shapley, LIME, and partial dependence plots, you can figure out how the model works and why.

I think we’ll see a lot of innovation in the ‘model interpretation’ space going forward.

Notes from the video:

  • IML R package
  • ML models have huge potential but are complex and hard to understand
  • In critical conditions (life vs death), you need to explain your decision
  • Current tools for Model Interpretation: Decision Trees, Rules, Linear Regressions
  • Needs a model agnostic method
  • Feature Importance @ interpreted level for the global model
  • Compute generalization error on dataset and model
  • Scored features, what is the effect on that feature on the fitted model?
  • Fit a surrogate model
  • Generate Partial Dependence Plots (visualize the feature importance)
  • For Local Interpretation, use LIME.
  • Now part of the R as iML package (written in R 6?)
  • What’s in the iml package? Permutation Feature Importance / Feature Interactions / Partial Dependence Plots / LIME / Shapley Values / Tree Surrogates
  • Shows the bike data set example

Bubble Grid vs Choropleth in R

Title: Bubble Grid vs Choropleth in R
Date: 2018-07-10 15:00
Slug: bubble-grid-vs-choropleth-r
Tags: R, Data Visualization
Twitter: neuralmarket
Author: Thomas Ott

I’ve always had a love/hate relationship with R but ever since I generated a Word Cloud using RapidMiner and R, I’ve come to like it’s visualization capabilities.

I stumbled across this recently written article by Jonas Schöley over at his blog jschoeley.github.io and found some really need Data Visualizations in R and code examples. The one using Bubble Grid vs Choropleth maps caught my eye. Check it out, it has ready to go R code.

Word Clouds in RapidMiner and R

There was a question from the RapidMiner Community on how to make a word clouds in RapidMiner using R. It’s really easy.

First you’ll need to make sure you have the Execute R extension installed and configured, then you need to download the “wordcloud” and “RColorBrewer” packages from R Cran Repository.

 

Finally, grab this sample XML and pop it in your RapidMiner Studio. Note: I saved the image to my desktop, you’ll have to repath it to where you want to save the PNG.

Word Clouds in RapidMiner