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  • “CAN YOU EXPLAIN THIS MODEL?” — When RANDOM FOREST Sounds Smart... But No One Actually Understands It

“CAN YOU EXPLAIN THIS MODEL?” — When RANDOM FOREST Sounds Smart... But No One Actually Understands It

High-performing models are impressive—until they block decisions, trust, and accountability.

Author

Glenda Carnate
May 01, 2026

Read time: 2.5 minutes

Once a model has become unexplainable, it is effectively unusable.

When the model achieved 94% accuracy, there was much celebration until one of the stakeholders asked, “Why did you make that prediction?” All of a sudden, there was silence, and heads turned.

The data scientist delved into feature importance and still had no idea why the model predicted what it did. That's when it dawned on everyone... we had a working model, and no one trusted enough to act on it.

Why "Great Models" Continue to Fall Short in Practice?

1. Precision ≠ Confidence
If you cannot explain how it works, stakeholders won't want to use it!

2. Risk is hidden by Complexity
Simply having more trees does not mean you have additional visibility.

3. When models are considered black boxes, this leads to delayed decision-making
When there is uncertainty about an output, no action will be taken because a decision hasn't been made yet.

4. Your job involves interpreting results
This is your number one job duty.

5. The simpler the model, the better it is for production
Readable beats out cleverness when working with data.

6. If you're unable to explain something clearly...
You don't fully grasp the concept.

💡Key Takeaway: 

If it works but you can’t explain why, that model won’t scale… it will become inoperable. In the real world, trust trumps accuracy every time.

👉 LIKE this if you have ever created an “accurate” thing that nobody used.

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👉 COMMENT: If you had the choice, would you pick accuracy or explainability? What is your reason?

👉 SHARE this with someone who assumes the only variable that matters is a model's performance.

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