Toward Interpretable Machine Learning

Cristoph Molnar shows off a couple of R packages which help interpret ML models:

Machine learning models repeatedly outperform interpretable, parametric models like the linear regression model. The gains in performance have a price: The models operate as black boxes which are not interpretable.

Fortunately, there are many methods that can make machine learning models interpretable. The R package imlprovides tools for analysing any black box machine learning model:

  • Feature importance: Which were the most important features?
  • Feature effects: How does a feature influence the prediction? (Partial dependence plots and individual conditional expectation curves)
  • Explanations for single predictions: How did the feature values of a single data point affect its prediction? (LIME and Shapley value)
  • Surrogate trees: Can we approximate the underlying black box model with a short decision tree?
  • The iml package works for any classification and regression machine learning model: random forests, linear models, neural networks, xgboost, etc.

This is a must-read if you’re getting into model-building. H/T R-Bloggers

Related Posts

Using xplain To Interpret Model Results

Joachim Zuckarelli walks us through the xplain package in R: The above XML produces the following output (don’t worry too much about the call of xplain(), we will discuss later on in more detail how to work with the xplain() function): library(car) library(xplain) xplain(call="lm(education ~ young + income + urban, data=Anscombe)", xml="") ## ## Call: ## lm(formula = education […]

Read More

Sentiment Analysis Of Hotel California

Sara Locatelli analyzes the lyrics to Hotel California using tidytext: Sentiment analysis is a method of natural language processing that involves classifying words in a document based on whether a word is positive or negative, or whether it is related to a set of basic human emotions; the exact results differ based on the sentiment […]

Read More

Leave a Reply

Your email address will not be published. Required fields are marked *


May 2018
« Apr