hyperparameters
Hyperparameters in the realm of Machine Learning refer to the configuration settings that are external to the model and need to be specified prior to the training process.
Unlike model parameters, which the algorithm learns from the data, hyperparameters are not learned but set by the machine learning engineer or researcher.
These hyperparameters significantly influence the performance and behavior of the model during training and inference.
The selection of appropriate hyperparameters is crucial as it directly impacts the model's ability to learn patterns and generalize to unseen data.
Common hyperparameters include learning rates, regularization strengths, number of hidden layers, number of neurons in each layer, and batch size.
Adjusting hyperparameters involves a process known as hyperparameter tuning, which often includes techniques like grid search, random search, or more advanced methods like Bayesian optimization.
Finding the optimal set of hyperparameters can be a time-consuming task, and the efficiency of this process greatly affects the model's efficacy.
A well-tuned model with appropriate hyperparameters can lead to improved accuracy, convergence speed, and overall performance, making understanding and fine-tuning hyperparameters a critical aspect of successful machine learning implementations.