Dan Shiebler

Introduction Many prediction problems can be framed as “given the knowledge that this sample belongs to categories \(A,B,C,\cdots,D\), predict something about this sample.” As a concrete example, suppose we would like to use linear regression to predict the value of a transaction based on a small set of categorical features... [Read More]

This post is part three in a three part series on the challenges of improving a production machine learning system. Find part one here and part two here. A/B Testing When engineers and data scientists optimize machine learning systems they often focus on improving offline metrics like cross entropy, ROC-AUC,... [Read More]

This post is part two in a three part series on the challenges of improving a production machine learning system. Find part one here and part three here. Adding New Features or Improving Existing Features A machine learning model is only as powerful as the features it is trained with.... [Read More]

This post is part one in a three part series on the challenges of improving a production machine learning system. Find part two here and part three here. Suppose you have been hired to apply state of the art machine learning technology to improve the Foo vs Bar classifier at... [Read More]

The ideas in this post were hashed out during a series of discussions between myself and Bruno Gavranović Consider a system for forecasting a time series in \(\mathbb{R}\) based on a vector of features in \(\mathbb{R}^a\). At each time \(t\) this system will use the state of the world (represented... [Read More]