In weighted sampling, each sample is given a weight, which determines the probability of it being selected. For example, if you have three samples, A, B, and C, and want them to be selected with the probabilities of 50%, 30%, and 20% respectively, you can give them the weights 0.5, 0.3, and 0.2. This method allows you to leverage domain expertise. For example, if you know that a certain subpopulation of data, such as more recent data, is more valuable to your model and want it to have a higher chance of being selected, you can give it a higher weight.
