Machine Learning Validation
Machine Learning Validation is a set of techniques and processes used to assess the performance, reliability, and generalization ability of machine learning models. It involves splitting data into training and testing sets, using methods like cross-validation to evaluate models on unseen data, and ensuring models are robust and not overfitting. This is critical for building trustworthy ML systems that perform well in real-world scenarios.
Developers should learn and use ML validation when building any predictive model to ensure it generalizes beyond the training data and avoids overfitting. It's essential in production ML systems, such as recommendation engines, fraud detection, or medical diagnostics, where poor performance can have significant consequences. Validation helps in model selection, hyperparameter tuning, and providing confidence metrics for deployment decisions.