A machine learning hyperparameter is a parameter whose values are selected before the training begins. Hyperparameters should not be confused with parameters.

Machine learning uses the label parameter to identify variables whose values are learned during training. Any variable that an AI engineer or machine learning engineer chooses before training begins can be considered a hyperparameter as long as its value does not change during training.

Some examples of model hyperparameters include Regularization of Logistic the Regularization Classifier, i.e., L1 or L2 penalty, a neural network's learning rate, support vector machines' C and sigma parameters, and a k-nearest neighbor's k.

What is hyperparameter tuning?

Optimal hyperparameter tuning involves finding the optimal values for hyperparameters in a learning algorithm and applying these values to any data set. The combination of hyperparameters maximizes the model's performance, minimizing a predefined loss function to produce better results.

What does hyperparameter tuning do?

A single training job runs multiple trials to tune hyperparameters. During each trial, your chosen hyperparameters are set within limits the limits specified for your training application. The AI Platform Training service keeps track of the results of each trial and adjusts it for subsequent trials. The results of all your trials will be presented to you along with the best configuration of values based on your criteria.

The training application defines all the information that your model requires. It would help if you defin the hyperparameters (variables) you wish to adjust, as well as their target values. The hyperparameter tuning process can be improved over time if you change only the objective function when tuning against similar models.

How to do hyperparameter tuning in python?

We have three hyperparameter tuning methods in python: Grid search, Random search, and Informed search.

• Grid Search: Grids are networks of intersecting lines that form squares or rectangles. Each square in a grid has a combination of hyperparameters, and the model must train itself on them all.
• Random Search: Random Search uses the same hyperparameter values we set for a grid search. There is, however, no training for every combination of hyperparameters in the model, but it selects them randomly.
• Informed Search: This method of tuning hyperparameters combines the advantages of both grid search and random search. However, it has its own disadvantages. The process of informed search differs from that of the grid, and random search in that informed search learns from previous iterations.

Conclusion

Tuning hyperparameters, you can increase your productivity by focusing on the most promising combinations of hyperparameters within your specified ranges. A hyperparameter tuning project is an integral part of any Machine Learning project, so it's always worth exploring.