undersamplingselect
Undersamplingselect is a technique used in machine learning, specifically in the context of imbalanced datasets. An imbalanced dataset is one where the number of observations for some categories is significantly lower than for others. This imbalance can cause machine learning models to be biased towards the majority class, leading to poor performance on the minority class, which is often the class of interest.
Undersamplingselect addresses this imbalance by reducing the number of instances in the majority class. Instead of
The goal of undersamplingselect is to create a more balanced training dataset without discarding too much