In my last post, I teased One-Hot Encoding and mentioned how to tell if your model is actually “accurate.” Well, today’s the day we look under the hood and realize that most models are basically lazy students… if you give them a chance to cheat, they will.

1. The “Dumb” Way to Talk to Computers: One-Hot Encoding

Last time we talked about Embeddings (the cool, multi-dimensional way to represent data). But before Embeddings became popular, we had One-Hot Encoding.

Think of it like a giant checklist of “Yes/No” questions. If you have three colors—Red, Blue, and Green—One-Hot Encoding represents Red as [1, 0, 0], Blue as [0, 1, 0], and Green as [0, 0, 1].

So with One-Hot Encoding, the idea is simple, but it’s kinda “dumb.” Why? Because in this math, Red is just as different from Pink as it is from a Tractor. There’s no nuance, no relationships, and if you have 10,000 categories, your spreadsheet suddenly has 10,000 extra columns. It’s a memory nightmare. We usually use this for simple stuff, but for complex “brain-like” behavior, we graduate to those Embeddings I mentioned before. Good to know the difference!

2. The Great Validation Trap: Don’t Split Randomly!

This is where most beginners (and even some pros) mess up. When you train a model, you split your data into a Training Set and a Validation Set, usually 80-20. Most people just do a “random shuffle.”

Stop doing that. Seriously.

If your data is a time series (like stock prices or sales over two years), and you split it randomly, your model “cheats.” It sees data from Tuesday, skips Wednesday, and sees Thursday. When you ask it to predict Wednesday, it’s not really “predicting”—it’s just interpolating because it already saw the future (Thursday).

Instead, split your data chronologically. Use the first 18 months to train, and the last 6 months to validate. This forces the model to actually learn patterns and predict an unknown future, rather than just memorizing gaps in the past.

3. The Danger of the “One Metric” Obsession

We all want a high “Accuracy” score. It feels good. But Accuracy is often a liar.

Imagine you’re building an AI to detect a super rare disease that only 1 in 1,000 people have. If I build a “model” that just says “No, you don’t have it” to every single person, my model is 99.9% accurate. I’d look like a hero on paper, but I’d be a disaster in the real world because I missed every single sick person.

What to do instead?

Don’t just look at Accuracy. Look at:

  • Precision and Recall: Are you catching the rare events? How many false alarms are you triggering?
  • The Confusion Matrix: A table that shows exactly where your model is getting confused.
  • The “Eye Test”: Actually look at the rows your model got wrong. Is there a pattern to its failures?

For those who don’t know those terms, think about this: instead of just checking if the model’s prediction is right or wrong, maybe you should consider the trend the model is going for too. Does it match the training data’s trend?

The Takeaway

Training a model isn’t just about hitting “Run” and watching the loss go down. It’s about setting up a fair test that the model can’t cheat on. If you give it a random split and a single metric to optimize, it will find the path of least resistance—which usually isn’t the path to true intelligence.

Think of it like a student who memorizes a practice test and aces it because they remember all the questions and answers. As soon as you give them the real test with shuffled questions or different wording, they’re completely lost because they never understood the concepts… they just memorized the practice test!