## Large Mistakes in Regression Problems

Many real world problems can be framed as regression: use a collection of features $$X$$ to predict a real-valued quantity $$y$$. However, this framing can obfuscate a very important detail: which kinds of mistakes are most important to avoid? Many factors can influence this, including how the predictions will be... [Read More]
Tags: Machine Learning, Regression, ML

## Predicting a Quantity with Categorical Features

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]
Tags: Machine Learning, Machine Learning Systems, ML, Features

## Improving a Machine Learning System (Part 3 - A/B testing)

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]
Tags: Machine Learning, Machine Learning Systems, A/B, A/B testing

## Improving a Machine Learning System (Part 2 - Features)

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]
Tags: Machine Learning, Machine Learning Systems, Features