Your ML model is 95% accurate. It's also useless. Here's why 👇 Built a model. Got

Your ML model is 95% accurate. It's also useless. Here's why 👇 Built a model. GotYour ML model is 95% accurate. It's also useless. Here's why 👇 Built a model. Got

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Moiz Ahmed Mansoori

• Apr 5

Your ML model is 95% accurate. It's also useless. Here's why 👇

Built a model. Got 95% accuracy. Celebrated.

Then checked the confusion matrix.

Out of 1000 predictions: 950 were class 0 (majority) 50 were class 1 (the ones that actually mattered)

The model learned one thing: predict class 0. Always. 95% accurate. 0% useful.

This is the accuracy trap. And it kills real ML projects.

The fix? Stop looking at accuracy. Start looking at:

F1 Score: balances precision and recall ROC-AUC: how well your model separates classes Confusion Matrix: shows exactly where it fails

I ran into this while building a fraud detection pipeline. Switching metrics changed everything.

Accuracy is a lie when your data is imbalanced.

What metric do you actually trust DM/comment me? 👇

ai machinelearning datascience

Akibul Hasan Akash

pro

• Apr 6

“Designers are cooked”

still getting DMs from founders to design their websites.

Because it’s never just about output.

It’s about taste. Clear thinking. Knowing what actually makes someone stop, understand, and feel something.

AI can generate fast. But it can’t judge what’s right.

And that gap? still wide.

saas Product Design saaswebsite UX Design Claude Figma Next.js

Vincent Pasili

pro

• Apr 6

Great insight!

Muhammad Usman

pro

• Apr 6

BayFi ✦ Explored a conceptual AI mobile experience for Evia, designed to make intelligence feel calm, creative, and alive. Soft gradient surfaces, spacious layouts, and subtle glass effects help reduce friction while keeping the interface expressive. From quick chat creation to project-based thinking, every interaction is optimized for clarity, speed, and trust.

ai mobileapp design

Akinkunmi Ayo

• Apr 6

well details

Moiz Ahmed Mansoori

• Apr 6

I cleaned data for 3 days. Model trained in 10 minutes. Nobody talks about this part of Data Science. 80% of the job is: — Missing values that make no sense — Columns named "col_1", "col_2", "col_final_FINAL" — Duplicate rows that shouldn't exist — Dates in 6 different formats The model is the easy part. The data is the real work. Next time someone says "just run a model on it" — you'll know why that sentence hurts.

AI Development machinelearning datascience