Supportsoft Glossary

Hyperparameter Tuning for Optimized AI Model Performance

Hyperparameters are the configuration settings that allow you to control how an AI or machine learning method works. In contrast to model parameters, which are automatically deduced from the data through the learning process, hyperparameters must be defined before the training phase/will influence how well the model behaves and performs generally and is therefore used to make predictions and how quickly a model trains.

Some common examples of hyperparameter configurations include the learning rate, size of the batch for training, number of layers within a neural network, and various methods of regularisation. Selecting the right combination of these hyperparameters is extremely important; hyperparameters that are not tuned properly can create large delays in training processes, cause the trained model to create inaccurate predictions, or prevent a trained AI model from accurately generalising to new inputs.

Hyperparameter tuning uses systematic processes to modify hyperparameters in order to get the highest performance for the model. In business applications, tuning hyperparameters helps ensure that the models generate reliable and actionable insights with the least amount of computational costs. Typical techniques for hyperparameter tuning include grid search, random search and automated techniques for finding tuneable hyperparameters.

Organisations can maximise the benefits of their investments in AI technology by properly tuning hyperparameters, thus ensuring that their AI models are optimally functioning in the deployed production environment.