6 machine learning pitfalls to avoid
Data science expert Andy Pulkstenis describes how to skip common machine learning mistakes.
English
Classifying messy documents: A common-sense approach (Part II)
In Part I of this blog post, I provided an overview of the approach my team and I took tackling the problem of classifying diverse, messy documents at scale. I shared the details of how we chose to preprocess the data and how we created features from documents of interest
Understanding your data: Missing at random versus missing not at random
In our last blog we explored the potential impact of missingness in data in terms of its impact on models which require complete case analysis. We took a simple view that data was missing with an equal, independent, probability for any given model input. This week we explore cases where