Machine Learning Hero

4.4 Hyperparameter Tuning and Model Optimization

Machine learning models utilize two distinct parameter types: trainable parameters and hyperparameters. Trainable parameters, such as weights in neural networks or coefficients in linear regression, are learned directly from the data during the training process.

In contrast, hyperparameters are predetermined settings that govern various aspects of the learning process, including model complexity, learning rate, and regularization strength. These hyperparameters are not learned from the data but are set prior to training and can significantly influence the model's performance and generalization capabilities.

The process of fine-tuning these hyperparameters is crucial for optimizing model performance. ...