Neural networks are the rockstars of machine learning — flashy, deep, and everywhere. But guess what? They don’t always win. In fact, there are plenty of real-world situations where good old Random Forests do a better job. This forum is all about those moments. When RFs outperform NNs — we want to hear about it.

Tabular Data: The Sweet Spot for Random Forests

Neural networks love images, audio, and text. But give them a spreadsheet full of customer data or medical records? Meh. Random Forests, on the other hand, thrive on this kind of structured data. They don’t need scaling, they handle missing values better, and they just… work. If you've used RFs on an Excel-style dataset and seen better results than NNs, let’s talk.

Small Datasets, Big Wins

Training a neural network on a tiny dataset is like teaching a toddler quantum physics — overkill and full of confusion. RFs are way more forgiving. They don’t need mountains of data to start performing well. They're perfect for those quick, real-world wins where data is limited but results matter.

Interpretable Models FTW

Ever tried to explain a neural network’s decision to your boss or a regulatory board? Good luck. Random Forests, though? You can actually see why they made a decision. Feature importance, decision paths — it’s all there. When transparency is key (looking at you, healthcare and finance), RFs are your best friend.

Speed, Simplicity, and Sanity

Neural networks can be slow to train and a pain to tune. RFs? Fast, easy, and you don’t need a PhD in hyperparameters. If you're working on a laptop without a GPU or need a model that just works without days of tweaking — RFs get the job done.

Let’s Share the Wins

Got a story where a Random Forest blew a neural network out of the water? Share your dataset, results, or just your thoughts. Whether you're a student, data scientist, or just curious, this is the place to learn when “simple” beats “deep.”