Description: Intuition-based tuning is an approach that relies on the practitioner’s experience and knowledge to optimize the hyperparameters of a machine learning model. This method is characterized by its flexibility and adaptability, allowing experts to apply their personal judgment and understanding of the problem at hand. Often, professionals use their intuition to select initial hyperparameter values based on patterns observed in previous data or the nature of the problem they are addressing. This approach can be particularly useful in situations where there are no clear guidelines or when working with complex models that require fine-tuning. Although intuition-based tuning may be less systematic than other methods, such as grid search or Bayesian optimization, its value lies in experts’ ability to quickly identify configurations that may work well, saving time and resources in the optimization process. However, it is important to recognize that this approach may be subject to biases and limitations, as it relies on individual perception and prior experience, which can lead to suboptimal decisions if not complemented with more rigorous methods.
