Description: Hyperparameter sensitivity refers to the degree to which a machine learning model’s performance is affected by changes in the values of its hyperparameters. Hyperparameters are configurations set before the model’s training and are not adjusted during the learning process. Examples of hyperparameters include learning rate, number of layers in a neural network, and batch size. The sensitivity of these parameters is crucial, as a small adjustment in their value can lead to significant variations in the model’s accuracy and generalization capability. A model with high sensitivity to hyperparameters can be challenging to optimize, requiring fine-tuning to achieve optimal performance. Conversely, a model with low sensitivity may be more robust and easier to handle, allowing good results to be achieved with less precise configurations. Understanding hyperparameter sensitivity is essential for researchers and practitioners in the field of machine learning, as it helps them identify which parameters are most critical to model performance and design more effective optimization strategies.
