Description: Adaptive sampling is a sampling method that adjusts the sampling strategy according to the characteristics of the data. This approach allows for more efficient and effective data collection, as it focuses on areas of greater interest or complexity within a dataset. Unlike traditional sampling methods, which typically apply a uniform strategy across the entire data space, adaptive sampling dynamically adapts as more data is collected. This means that if specific patterns or characteristics are detected in the data, the sampling process can change to focus on those areas, thereby optimizing the quality and relevance of the collected information. This method is particularly useful in contexts where data is heterogeneous or where certain areas are expected to exhibit more variability or interest than others. In the realm of machine learning, statistics, and data analysis, adaptive sampling can improve the efficiency of model training by reducing the number of samples needed to achieve a desired performance, which in turn can decrease computation time and required resources.
