Description: The Intuition-Based Approach for hyperparameter optimization is a method that relies on the experience and prior knowledge of the practitioner rather than solely depending on automated techniques or complex algorithms. This approach is based on the premise that experts in the field can make informed adjustments to a model’s hyperparameters, guided by their understanding of the nature of the problem and the characteristics of the data. Often, this method involves the manual selection of values for key hyperparameters, such as learning rate, number of layers in a neural network, or batch size, based on intuition about how these parameters may influence model performance. While it may be less systematic than other methods, such as grid search or Bayesian optimization, the intuition-based approach can be effective in various situations, including when time is limited or when rapid iteration is required. Additionally, this approach allows practitioners to learn and adapt to the peculiarities of their data, which can result in a deeper understanding of the model and its behavior. However, it is important to recognize that this method can be subjective and, in some cases, may lead to suboptimal results if not combined with rigorous performance evaluation of the model.
