How to Beat Proprietary LLMs With Smaller Open Source Models
Building your AI applications around open source models can make them better, cheaper, and faster
A Guide to Structured Generation Using Constrained Decoding
The how, why, power, and pitfalls of constraining generative language model outputs
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
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
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
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
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.
