Hyperparameters
Machine learning is a branch of artificial intelligence in which computers learn to perform certain tasks based on data. When training a machine learning model, there are several aspects that determine its performance. One of these aspects is the model's hyperparameters.
What exactly are hyperparameters?
Hyperparameters are the settings of a machine learning model that are not adjusted while training the model. Instead, they are preset by the user. Examples of hyperparameters include the number of hidden layers in a neural network, the batch size during training the model and the learning speed of the algorithm.
Choosing the right hyperparameters is critical to the performance of the model. If the hyperparameters are not well matched to the specific task, the model may not perform well. On the other hand, if the hyperparameters are properly tuned, the model can perform better and make more accurate predictions. Tuning the hyperparameters is often an iterative process of trying different values to determine which settings work best for the specific task. This process is often referred to as hyperparameter optimization and can be performed manually or using automated tools.
