Summary
In this chapter, we delved into CSL, an alternative to oversampling and undersampling. Unlike data-level techniques that treat all misclassification errors equally, CSL adjusts the cost function of a model to account for the significance of different classes. It includes class weighting and meta-learning techniques.
Libraries such as scikit-learn, Keras/TensorFlow, and PyTorch support cost-sensitive learning. For instance, scikit-learn offers a class_weight hyperparameter to adjust class weights in loss calculation. XGBoost has a scale_pos_weight parameter for balancing positive and negative weights. MetaCost transforms any algorithm into its cost-sensitive version using bagging and a misclassification cost matrix. Additionally, threshold adjustment techniques can enhance metrics such as F1 score, precision, and recall by post-processing model predictions.
Experiments with various data sampling and CSL techniques can help determine the best approach. We’ll extend...
