A story about overfitting, insecurity, and why pleasing everyone makes you worse at your job.
You know that friend who mirrors everyone in the room?
They change their tone depending on who they're talking to. They agree with whatever’s said, even if it contradicts what they said five minutes ago.
They’re charming. But not reliable. You don’t really know what they stand for.
Frankly, neither do they. That’s overfitting.
The student who memorized the textbook
In machine learning, overfitting happens when a model learns the training data too well.
It doesn't just learn the core patterns. It learns the quirks, the noise, the weird one-off exceptions.
It becomes so obsessed with acing the test questions it has already seen that it loses the ability to answer a new question—to generalize.
It’s like someone who rehearses for a date by memorizing answers from a Reddit thread. When the real conversation starts, they freeze.
They weren't learning to connect. They were performing to be liked.
The danger of perfect grades
Here’s the thing about overfitting: at first, it looks like success.
The model aces the training metrics. Accuracy? 99.9%. Loss? Nearly zero.
But then you test it on data from the real world, and it collapses.
Why? Because it never actually understood anything. It just memorized the answers.
It confused familiarity with insight.
Our inner people-pleaser
This isn’t just a bug in a neural network. It's a deeply human instinct.
We want to be liked. We want to be right. So we adapt, we mimic, we play to the crowd.
But when you optimize only for applause, not for understanding, you lose your core identity.
The same way a model trained without constraints will fail under new conditions, a person who never learns to say "no"—or to sit with ambiguity—will break when the script runs out.
The cure: a little chaos
So how do we fix this? How do we teach a model to be flexible and robust?
We introduce a little chaos. We force it to generalize.
In code, these are called regularization techniques:
- Dropout: Randomly ignoring some neurons during training, so the model can't rely too much on any single signal.
- Early stopping: Halting the training process right before the model starts memorizing noise instead of learning patterns.
- Data augmentation: Adding slightly modified copies of the training data, so the model learns to recognize the subject, not just the specific picture.
In life, the techniques are surprisingly similar:
- Trying new things and being willing to be wrong.
- Getting comfortable with uncertainty.
- Learning from variety, not just from sterile repetition.
You will start to see this everywhere
That Spotify playlist that recommends a song that sounds like your taste but completely misses the vibe? overfitting.
That chatbot that just parrots your prompt back to you without adding any real nuance? overfitting.
That resume stuffed with keywords that doesn't actually say anything about the person? you guessed it.
You don’t need to be liked by every data point.
You need to make sense of the world beyond them.
It’s the difference between memorizing the map and knowing the territory.