2 Typical Failure AI models

Choosing a fit model ain’t easy, even true in #AI

A model fails because it’s either too weak to learn the signal, or too eager to learn the noise, yo know 🤭

2 TYPICAL FAILURE AI MODELS

In practice, it’s about preventing 2 failures that kill your AI before it even starts:

❌ Failure #1: Underfitting path: the model that didn’t learn enough

This is like the student who barely studies, lazy & laid-back.

They don’t understand the topic, so they get questions wrong even when the exam is easy 😂.

The model is too simple to capture the real pattern.

So it performs poorly on training data & also on new data.

Like everywhere, incompetency shows up fast!!!

The fix:

• Help it learn more signal.
• Add better features, do feature engineering, reduce noise/outliers, increase model capacity
• or just collect more data.

👉 Basically, give it a better education 🤷‍♂️.

❌ Failure #2: Overfitting path: the model that memorizes

This is the student who just memorizes, just like CRAM, just like cram.

They look brilliant on the practice test, but the real exam changes one detail & they fail hard.

The model “memorizes” noise in the training data.

It looks great during training, then collapses in the real world when conditions shift even slightly.

The fix: Stop the cheating. Get more data, remove weak/redundant features & use regularization.

👉 Diversification is needed.

2. PREVENTIONS

The two boring habits that prevent both failures:
❎Habit A: Keep a validation set.

Think of it as a “mock exam” that your model has never seen.

If performance starts dropping there, you’re watching overfitting happen in real time, yo know.

Then you adjust hyperparameters (like lower learning rate) or simplify the model before it’s too late.

❎Habit B: Scale features.
If one feature has huge numbers & another is tiny, the huge one dominates learning.

Scaling (min–max or z-score) makes training more stable & helps optimization converge faster.

Simple but critical!!!

🌸 My POV:
Then choose methods based on the job-to-be-done.

Like everywhere saying: choose an appropriate AI tool for the actual problem, not the coolest one 🤷‍♂️🤷‍♂️🤷‍♂️.

It turns your AI from a prototype into something you can actually trust in business decisions, not just highly overstimulated tools that look impressive in demos but fail in production.

TOMMY🙏

This is as for informational & educational purpose, No liability for actions taken. Nothing in this article constitutes legal, compliance, or regulatory advice.

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