Downsampling — многозначный термин, который может означать снижение разрешения, детализации или объема информации в данных либо уменьшение числа примеров из чрезмерно представленного класса. Он используется для повышения эффективности, управления дисбалансом классов и уменьшения влияния избыточных или повторяющихся данных.
Overloaded term that can mean either of the following: - Reducing the amount of information in a feature in order to train a model more efficiently. For example, before training an image recognition model, downsampling high-resolution images to a lower-resolution format. - Training on a disproportionately low percentage of over-represented class examples in order to improve model training on under-represented classes. For example, in a class-imbalanced dataset, models tend to learn a lot about the majority class and not enough about the minority class. Downsampling helps balance the amount of training on the majority and minority classes. See Datasets: Imbalanced datasets in Machine Learning Crash Course for more information.