Recall and Precision – A Practical Case Against Memorization

The Internship Interview Paradox

You ever find yourself in an interview, and they ask you something you could literally Google in five seconds? Yeah, me too. It’s wild, right? We’re living in an age where everything’s at our fingertips, but somehow, we’re still expected to memorize things like we’re back in high school.

It doesn’t make sense, especially when, in real-world jobs, you don’t need to remember every little detail off the top of your head. You just look it up! It’s all about knowing how to apply that information when you need it, not spitting out formulas from memory. But nope—interviews love to quiz you like it’s a game show.

A Classic Example

Here’s a quick example that sums it up perfectly. You’ve probably been there, too. You’re in an interview, and they hit you with: “What’s the formula for recall and precision?”

On the surface, it seems harmless. But the thing is, you could just Google that. What’s more important, though, is understanding what these metrics actually mean and why they matter.

The Stuff That Actually Matters

Instead of grilling candidates on formulas, it’d make way more sense to ask, “Can you explain what recall and precision are, and why they’re important?” That would show who actually understands the concepts.

Let’s break it down, just for fun:

• Recall is all about how many relevant instances your model is catching. So, if you’re working on something like a medical diagnosis tool, low recall means you’re missing patients who actually have the condition. Not great.

\[\begin{equation} Recall = \frac{True\space Positives}{True\space Positives + False\space Negatives} \label{eq:recall} \end{equation}\]

• Precision, on the other hand, focuses on how accurate those predictions are. Imagine a spam filter that keeps flagging your important emails as spam. Super annoying, right? That’s what happens when precision is low.

\[\begin{equation} Precision = \frac{True\space Positives}{True\space Positives + False\space Positives} \label{eq:precision} \end{equation}\]

And here’s the kicker: there’s usually a trade-off between the two. If you try to improve recall, you might end up tanking your precision, and vice versa. So, it’s not as simple as just knowing the formula—it’s about balancing these metrics based on what’s important for the task at hand.

So, What’s the Point?

At the end of the day, testing someone’s ability to regurgitate info isn’t really helpful. It doesn’t show if they understand the bigger picture or know how to solve real problems. In today’s world, where all the info we could ever need is a click away, interviews should be testing how we think, not how well we can memorize stuff.