Model training is a fundamental stage in machine learning, where an algorithm learns to recognize patterns and relationships within a dataset. The goal of training is to develop a model that can make accurate predictions or classifications when exposed to new, unseen data. This process involves iterative adjustments to model parameters, optimization of learning rates, and monitoring of performance metrics to ensure generalization.
Training a model effectively requires several considerations, including selecting an appropriate algorithm, preparing high-quality data, tuning hyperparameters, and preventing overfitting. The quality of training directly impacts the model’s ability to generalize beyond the training dataset, making it essential to carefully plan and execute each step. In this section, we will explore various aspects of model training, from data preparation to optimization techniques that enhance performance.