What is a downsampling?
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.
downsampling explained in plain English
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.
Example
Practitioners refer to downsampling when building, training, or evaluating machine learning systems. It appears in research papers, product documentation, and technical discussions about AI capabilities and limitations.
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