Downsampling from a Data Science Perspective

Downsampling in data science and data processing is as follows (this excludes the DSP, or digital signal processing, technical definition of downsampling - which is similar in spirit but differently defined).

Downsampling involves reducing the number of data points in a data set to enable comparability (sometimes referred to as "balancing the data").  This helps machine learning models avoid bias towards a dominant class.

Various approaches to downsampling (e.g. random downsampling) are described in this IBM article.