Hyperparameters are variables external to and not directly related to data sets of know outcomes that are used to train Machine Learning models. hyperparameter is a configuration variable that is used to optimize model performance.

Automated Machine Learning (AutoML) systems such as Squark tune hyperparameters automatically. Data scientists who build models manually can write code that controls hyperparameters to seek ways to improve model performance.

Examples of hyperparameters are:

  • Learning rate and duration
  • Latent factors in matrix factorization
  • Leaves, or depth, of a tree
  • Hidden layers in a deep neural network
  • Clusters in a k-means clustering
  • The k in k-nearest-neighbors