to use random forests: when you want a strong baseline with minimal hyperparameter tuning

Why not always use the best single tree for predictions?

Imagine you're trying to predict whether it'll rain tomorrow. You have a weather app that uses a single weather model, but it sometimes gets it wrong, especially if the weather pattern is unusual.

Think of it like asking a bunch of friends for their opinion on the weather. Instead of relying on one friend's guess, you get a bunch of different opinions, which helps you make a better overall prediction.

Example

Your weather app's single model predicted a 70% chance of rain, but when you checked with five friends, their guesses averaged out to a 60% chance of rain.

Remember this

Using a bunch of different models (random forests) helps you get a more accurate prediction by averaging out individual mistakes.

Text adapted from Wikipedia, licensed under CC BY-SA 4.0.

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