What is candidate sampling

Candidate sampling is an important concept in machine learning, and it refers to the process of selecting a subset of data points from a larger dataset to be used in training a model. This technique is used to reduce the computational complexity of training a model, as well as to improve the accuracy of the model. Candidate sampling can be used for both supervised and unsupervised learning.

In supervised learning, candidate sampling is used to select a subset of data points that represent the most relevant features of the dataset. This allows the model to focus on the most important features and ignore the noise in the data. For example, if a dataset contains a large number of features, the model can be trained on a subset of those features that are most relevant to the task. This will reduce the computational complexity of the model and improve its accuracy.

In unsupervised learning, candidate sampling is used to select a subset of data points that represent the most diverse and interesting features of the dataset. This allows the model to explore different aspects of the data, which can lead to better results. For example, in clustering, candidate sampling can be used to select a subset of data points that represent different clusters. This will allow the model to explore different clusters and find patterns in the data.

Overall, candidate sampling is an important concept in machine learning. It can be used to reduce the computational complexity of training a model and improve its accuracy. It can also be used to explore different aspects of the data in unsupervised learning. By understanding how candidate sampling works and how it can be used, machine learning practitioners can make better use of their datasets and improve the performance of their models.