Description: Weighted sampling is a method of sample selection that assigns different weights to observations, allowing some to have a higher probability of being chosen than others. This approach is particularly useful in contexts where data is imbalanced or where certain samples are more representative or relevant to the problem at hand. In the realm of machine learning, weighted sampling can help improve training quality by prioritizing examples that are more difficult to classify or that contain critical information. In distributed learning, this method allows models to be trained more effectively by considering the diversity of data across different sources, ensuring that model updates adequately reflect the importance of each dataset. In hyperparameter optimization, weighted sampling can be used to more efficiently explore the search space, focusing on parameter combinations that have proven to be more promising in previous iterations. In various programming frameworks, weighted sampling can be easily implemented using specific functions that allow weights to be assigned to samples, thus facilitating its integration into machine learning models.
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