What feature is recommended for extremely large databases to decrease model training time?

Sampling is a crucial technique for handling extremely large datasets when it comes to model training in data mining and machine learning contexts. The primary goal of sampling is to reduce the size of the dataset while still retaining the essential characteristics and patterns of the original data. This is especially beneficial in scenarios where the complete dataset is too large to be processed in a reasonable time frame or requires excessive computational resources.

By selecting a representative subset of the data, sampling allows for faster training of models, as it decreases the volume of data that needs to be analyzed. This reduction in data often leads to a significant decrease in computation time, enabling quicker iterations and facilitating rapid experimentation without sacrificing the integrity of the model.

While other techniques such as aggregation, stratification, and binning can be useful for different purposes, they do not primarily focus on reducing the size of the dataset for the sake of faster model training. Aggregation summarizes data, stratification organizes data into distinct groups, and binning categorizes continuous variables into discrete intervals. These methods may help with preprocessing or enhancing model performance but do not directly address the challenge of training on extremely large databases as effectively as sampling does.