Imagine you have a bunch of toys and you want to find out which ones are the best. You could look at each toy individually and rate it, but that would take a lot of time. Instead, you can use the random subspace method.
Here's how it works: first, you pick a few toys at random from your pile. Then, you look at those toys and rate them based on how much you like them. This is like creating a small "subspace" of toys.
Next, you pick another set of toys at random and rate them too, creating another subspace. You keep doing this until you have rated all of the toys.
Once you have rated all of the toys in each subspace, you can look at the results and see which toys were rated the highest overall. These are the best toys!
The random subspace method is often used in machine learning to help figure out which variables or features are most important for predicting something. Just like rating toys, you take a few variables at random and see how well they predict the outcome you're interested in. Then, you do this with different sets of variables until you have a good idea which ones are most important in predicting the outcome.