If you’ve spent any time learning about natural language processing (NLP), you’ve probably come across the term “word embeddings.” Simply put, word embeddings turn words into numbers — or more accurately, dense vectors — that capture something about their meaning. Words like “king” and “queen” wind up closer together in this numeric space than, say, “king” and “carrot.” It’s a neat trick that helps computers understand language better.
But here’s something you might not have thought about: what if we used a similar idea for tabular data?
Tabular data is everywhere. Think about the spreadsheets, databases, or CSV files you work with — rows and columns packed with numbers, categories, dates, and so on. Usually, when we prepare tabular data for machine learning, we rely on standard techniques like one-hot encoding for categorical variables or normalization for numbers. These methods work, but they often treat each category as totally separate and ignore any relationships between them.
That’s where borrowing a page from NLP can help. Instead of treating each category as its own isolated entity, we can create embeddings for these categories. In other words, we represent each category as a vector that captures some of its relationships with other categories.
Why does this matter? Because it can reveal hidden patterns and make models smarter. For example, suppose you have a ‘city’ column with lots of different cities. Cities that share similar customer behavior, climate, or demographics might get similar embeddings. These vectors then help your model pick up on those subtle connections without manually encoding them.
I remember trying this with a dataset for a side project. Initially, my model struggled with the ‘product type’ categorical variable since there were dozens of categories. Switching to embedding those categories improved performance noticeably. It wasn’t magic — just a more nuanced way to present the data.
Here’s the quick takeaway:
– Word embeddings aren’t just for text. They can be applied to any categorical data.
– Using embeddings for tabular data can capture relationships traditional encodings miss.
– This approach can sometimes lead to better model accuracy with less manual feature engineering.
If you’re dabbling with machine learning and working on tabular datasets, consider testing out embeddings for your categorical features. It might add a subtle but valuable edge to your models.
At the end of the day, it’s another tool in your toolbox. No hype, just a useful trick that comes from how NLP folks have tackled a similar problem. Who knew word embeddings could travel beyond words?