In theory, using class weights or downsampling with recalibration could yield the same results if the machine learning model learns all classes equally well. In practice, you’ll likely achieve similar outcomes if the subsampling ratio w isn’t too large (i.e., when the difference in the number of samples per class isn’t too extreme). However, if ww is too large, the model may struggle to effectively learn the minority class. If the loss from a particular sample/group of samples is minimal, the model might ignore it.

Class weights, upsampling, or downsampling are generally used to ensure balanced class representation, helping the model consider all classes equally important and allowing it to rely on features rather than frequency statistics for discrimination. However, these methods are not explicitly used for calibration. Calibration is usually performed later using techniques specifically designed for that purpose.

TLDR: No for large w

In theory, using class weights or downsampling with recalibration could yield the same results if the machine learning model learns all classes equally well. In practice, you’ll likely achieve similar outcomes if the subsampling ratio w isn’t too large (i.e., when the difference in the number of samples per class isn’t too extreme). However, if w is too large, the model may struggle to effectively learn the minority class. If the loss from a particular sample/group of samples is minimal, the model might ignore it.

Class weights, upsampling, or downsampling are generally used to ensure balanced class representation, helping the model consider all classes equally important and allowing it to rely on features rather than frequency statistics for discrimination. However, these methods are not explicitly used for calibration. Calibration is usually performed later using techniques specifically designed for that purpose.