
How To Handle Imbalanced Data In XGBoost Using scale_pos_weight In Python
In machine learning, we often come across datasets where the number of observations in one class significantly outweighs the other. This is known as imbalanced data. For instance, in a dataset of credit card transactions, the number of fraudulent transactions (positive class) is usually much smaller than the number of legitimate transactions (negative class). This is also an example of a binary classification task, which is a common type of machine learning problem....