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

Bayesian Neural Networks

Yoel Zeldes thinks about neural networks from a different perspective: The term logP(w), which represents our prior, acts as a regularization term. Choosing a Gaussian distribution with mean 0 as the prior, you’ll get the mathematical equivalence of L2 regularization. Now that we start thinking about neural networks as probabilistic creatures, we can let the fun […]

Read More

Microsoft R Open 3.5.1

David Smith announces Microsoft R Open 3.5.1: Microsoft R Open 3.5.1 has been released, combining the latest R language engine with multi-processor performance and tools for managing R packages reproducibly. You can download Microsoft R Open 3.5.1 for Windows, Mac and Linux from MRAN now. Microsoft R Open is 100% compatible with all R scripts and packages, and works with […]

Read More

Categories

May 2018
MTWTFSS
« Apr Jun »
 123456
78910111213
14151617181920
21222324252627
28293031