November 2019
Intermediate to advanced
346 pages
9h 36m
English
In machine learning, a hyperparameter is a parameter whose value is set before the training process begins. For example, the choice of learning rate of a gradient boosting model and the size of the hidden layer of a multilayer perceptron, are both examples of hyperparameters. By contrast, the values of other parameters are derived via training. Hyperparameter selection is important because it can have a huge effect on the model's performance.
The most basic approach to hyperparameter tuning is called a grid search. In this method, you specify a range of potential values for each hyperparameter, and then try them all out, until you find the best combination. This brute-force approach is comprehensive ...