Model Validation
Model validation is a process in software development, particularly in data science and machine learning, that involves evaluating the performance and accuracy of a trained model using independent data not seen during training. It ensures that the model generalizes well to new, unseen data and helps detect issues like overfitting or underfitting. Common techniques include train-test splits, cross-validation, and holdout validation.
Developers should learn model validation to build reliable and robust machine learning models that perform consistently in real-world applications, such as predictive analytics, fraud detection, or recommendation systems. It is essential for assessing model quality, tuning hyperparameters, and ensuring compliance with regulatory standards in industries like finance or healthcare.