Training data

Training data is the foundation upon which machine learning models are built. It is used to teach a model the relationships between inputs and outputs, enabling it to make predictions or decisions on new, unseen data. The quality, quantity, and representativeness of the training data are critical factors influencing the performance of the trained model. A larger, more diverse and accurately labeled training dataset generally leads to a more robust and accurate model. During training, the model iteratively adjusts its internal parameters to minimize the difference between its predictions and the actual outputs in the training data, a process often guided by a loss function and optimization algorithm. The data is split into training, validation, and test sets, with the validation set used to tune hyperparameters and prevent overfitting and the test set used to evaluate the model's final performance on unseen data.