How do you handle greater weights toward the majority class in an imbalanced classification problem?

In an imbalanced classification setting, the goal is usually to give the minority class more influence during training, but if you intentionally want to give the majority class higher weight, you can still use class weighting. In scikit‑learn you pass a class_weight dictionary to estimators (e.g., {'0': 2, '1': 1} where 0 is the majority class). The loss function is multiplied by these weights, so the model penalizes mistakes on the majority class more heavily. You can also adjust the sample_weight array for each training instance. Other techniques include undersampling the minority class or oversampling the majority class, but these change the data distribution rather than the loss. When using gradient‑boosted trees (XGBoost, LightGBM), you can set scale_pos_weight or class_weight. The time complexity of training remains O(n) for most algorithms, but the memory overhead increases with the number of weighted samples. Always evaluate with metrics that reflect class imbalance, such as precision‑recall, F1, or ROC‑AUC, rather than plain accuracy, to confirm the weighting has the desired effect.