Spike-and-slab regression

Imagine you have a toy box filled with all kinds of toys. Now you want to find out which toys are your favorite and which ones are just okay.

Spike-and-slab regression is like a toy box for grown-ups. Instead of toys, it helps us find out which factors (or toys) are important in predicting an outcome (or making us happy).

The spike represents the important factors (or the toys that you love the most), and the slab represents the less important factors (or the toys that are just okay).

Using this method, we can determine which factors are likely to have a big impact on the outcome and which ones might not be as important. Just like how you can pick out your favorite toys from the toy box, spike-and-slab regression helps us pick out the most important factors from a large dataset.