Top 6 Errors Novice Machine Learning Engineers Make

In machine learning, there are many ways to build a product or solution and each way assumes something different. Many times, it’s not obvious how to navigate and identify which assumptions are reasonable. People new to machine learning make mistakes, which in hindsight will often feel silly.

I’ve created a list of the top mistakes that novice machine learning engineers make. Hopefully, you can learn from these common errors and create more robust solutions that bring real value.

1. Taking the default loss function for granted

Mean squared error is great! It really is an amazing default to start off with, but when it comes to real-world applications this off-the-shelf loss function is rarely optimum for the business problem you’re trying to solve.

Take for example fraud detection. In order to align with business objectives what you really want is to penalize false negatives in proportion to the dollar amount lost due to fraud. Using mean squared error might give you OK results but will never give you state-of-the-art results.

Becoming a Machine Learning Engineer | Step 3: Pick Your Tool Check out this article to learn about the different ML tools at your disposal.