Want to learn what to consider when choosing an error metric for your machine learning model? Read this post I wrote on Cardinal Path’s blog! The main considerations are: Do we want to punish overestimates or underestimates more heavily? Are there segments which have greater costs associated with incorrect predictions? Should we punish larger errors […]
Tag Archives: process
Data Leakage: How Data Collection Impacts the Decisions We Make and Vice Versa
I wrote this post on Cardinal Path’s blog. There is a lot to consider when building a model: Data leakage. Data leakage occurs when the data you are using to train a machine learning algorithm happens to include unexpected information related to what you are trying to predict, allowing the model or algorithm to make unrealistically […]
When all you have is a hammer, everything looks like a nail: choosing the right tool for the job
Check out this post that I wrote over on Cardinal Path’s blog that discusses finding the right tool for the job: A few weeks ago, a coworker asked me for some help with a data cleaning task. I consider him to be one of the best Tableau users in our office, and someone I frequent […]