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: machine learning
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 […]