Model Training
Model Training refers to the process of teaching a machine learning model to recognize patterns in data by adjusting its parameters based on input-output examples. During training, the model learns to map input features to target labels (supervised learning) or find hidden structures in data (unsupervised learning). The quality of model training depends on factors such as data quality, algorithm choice, hyperparameter tuning, and training duration. This tag is vital for researchers, data scientists, and developers looking to optimize model performance. Engaging with Model Training helps improve accuracy, efficiency, and generalization of machine learning models in real-world applications.
