Model training

Model training is the process of teaching a machine learning system to recognize patterns by analyzing data. During this phase, the model adjusts its internal parameters to minimize prediction errors and improve accuracy.

The process involves feeding the model with a dataset, calculating loss values, and updating weights using optimization algorithms. Training continues until the model performs well on both the training and validation sets, showing it can handle new, unseen data accurately.

Factors affecting training time include the size of the dataset, model complexity, hardware resources, and number of training epochs (one complete pass of the dataset through the model).