Overfitting

Overfitting is a term that’s used when a model or algorithm tries a little too hard to learn from a specific set of data. Imagine you’re trying to take a picture that represents what your entire school looks like, but you only take a photo of your classroom. That wouldn’t really be a good representation of the whole school, right? Overfitting is like that. It happens when a data model is so closely fitted to the little details of the data it was trained on that it misses the bigger picture.

Overfitting can be a problem in business intelligence and data analytics because it can make a model really good at handling the data it already knows, but pretty bad at making predictions about new, unseen data. It’s like memorizing answers to a test without understanding the subject—you might ace one test but fail a slightly different one.

To avoid overfitting, data scientists use various techniques to keep their models balanced. They make sure the model is complex enough to detect patterns but simple enough to apply those patterns to new data accurately. This helps businesses make predictions and decisions that are truly useful, not just ones that look good because they worked on one set of data.