Mastering Numerical Computing with NumPy

Hyperparameters

Hyperparameter could be considered as high-level parameter which determines one of the various properties of a model such as complexity, training behavior and learning rate. These parameters naturally differ from model parameters as they need to be set before training starts.

For example, the k in k-means or k-nearest-neighbors is a hyperparameter for these algorithms. The k in k-means denotes the number of clusters to be found, and the k in k-nearest-neighbors denotes the number of closest records to be used to make predictions.

Tuning hyperparameters is a crucial step in any machine learning project to improve predictive performance. There are different techniques for tuning, such as grid search, randomized search and bayesian ...