Oversampling

Oversampling is a technique used when you’re dealing with data that’s kind of unbalanced. Picture a bag of jellybeans where most of them are red and only a few are blue. If you wanted to understand all flavors equally, you’d need more blue jellybeans in the mix. In data analytics, oversampling means increasing the presence of certain, less common data to balance things out. For example, in a survey, if only a few people chose a particular option, analysts might ‘oversample’ to make that option more represented in the data they’re studying.

In business intelligence, oversampling helps when you’re trying to make predictions or understand trends about the less common stuff. It’s like giving a microphone to the quiet people in a room to make sure their voices are heard. This can be super important for things like fraud detection, where fraudulent transactions are rare compared to normal ones. To build a model that’s good at spotting fraud, you’d use oversampling to make sure the model learns enough about those rare cases.

However, just like adding too much of one flavor can mess up a recipe, oversampling has to be done carefully to avoid skewing the results. If it’s overdone, it can make those rare events seem more common than they actually are, leading to incorrect conclusions. So, data scientists use oversampling to get a clearer picture while making sure they still reflect reality accurately.