Description: Random sampling is a statistical technique used to select a subset of data points randomly, ensuring that each element of the population has an equal probability of being chosen. This methodology is fundamental in various fields of data analysis and machine learning, where an adequate representation of data is required to train models effectively. By applying random sampling, the aim is to avoid biases in data selection, which can lead to poor model performance. This technique allows models to learn general patterns rather than memorizing specific examples, which is crucial for generalization on unseen data. Additionally, random sampling can help reduce training time by decreasing the amount of data processed in each iteration, thus facilitating the optimization of model parameters. In the context of machine learning, random sampling is commonly used in data preparation, where data points are randomly selected to create training, validation, and test sets, ensuring that the model has balanced exposure to different classes and characteristics of the data.
