Help Your Model Learn the True Signal

Some data points can disproportionately disrupt a machine learning model, skewing its ability to learn true patterns and generalize to new information. An effective way to identify these disruptive observations is by using an algorithm-agnostic method inspired by the classic statistical concept of Cook's Distance. This technique calculates an "influence score" for each point by measuring how much the model's overall predictions change when that single point is removed from the training data. By flagging and managing the points with the highest influence scores, you can build more robust and reliable models that better capture the dominant signal in your data.