ML Model Validation
ML Model Validation is the process of evaluating a machine learning model to determine how well it performs on unseen data and whether it meets the required accuracy, reliability, and performance standards before deployment.
What is ML Model Validation?
ML model validation involves testing a trained model using data that was not used during training. Teams may use techniques such as holdout validation, cross-validation, and evaluation metrics appropriate to the task. Validation can also assess factors such as robustness, bias, and performance across different data conditions.
Why is ML Model Validation Important?
Model validation helps identify overfitting, unexpected errors, and performance gaps before a model is used in production. It gives teams greater confidence that the model can generalize to real-world data and perform consistently under expected conditions.
Common use cases
ML model validation is commonly used for classification, regression, forecasting, fraud detection, recommendation systems, computer vision, natural language processing, and other machine learning applications.