Machine Learning

Apr
08

A Guide to Structured Generation Using Constrained Decoding

The how, why, power, and pitfalls of constraining generative language model outputs
14 min read
Dec
31

How Shapley Values Work

In this article, we will explore how Shapley values work - not using cryptic formulae, but by way of code and simplified explanations
10 min read
Aug
13

Industry Perspective: Tree-Based Models vs Deep Learning for Tabular Data

Tree-based models aren't just highly performant - they offer a host of other advantages
3 min read
May
16

Supervised Clustering: How to Use SHAP Values for Better Cluster Analysis

Cluster analysis is a popular method for identifying subgroups within a population, but the results are often challenging to interpret
9 min read
Jan
02

Utility vs Understanding: the State of Machine Learning Entering 2022

The empirical utility of some fields of machine learning has rapidly outpaced our understanding of the underlying theory: the models
12 min read
Nov
01

Explaining Machine Learning Models: A Non-Technical Guide to Interpreting SHAP Analyses

With interpretability becoming an increasingly important requirement for machine learning projects, there's a growing need for the complex outputs of techniques such as SHAP to be communicated to non-technical stakeholders.
12 min read