Hyperparameter

💡What is a Hyperparameter and How is it Different from a Base Matrix?

• Hyperparameters are configuration values used to control the training process of a model—such as learning rate (LR), batch size, and number of epochs. Unlike the Base Matrix, where a fixed learning rate is typically used (based on what is assumed to best fit the model), hyperparameter tuning allows for experimentation with a range of values.
• In hyperparameter mode, you can define multiple parameter options—such as setting a minimum and maximum range for the learning rate. This enables the system to explore various combinations during training, ultimately helping to identify the most effective configuration. By comparing performance across these variations, the model can be trained more accurately and efficiently.
• The key benefit is that out of all the sorted parameter combinations, the best-performing one can be selected for inference, leading to improved and more reliable model performance.