Calculating model accuracy is a critical part of any machine learning project, yet many data science tools make it difficult or impossible to assess the true accuracy of a model. Often tools only validate the model selection itself, not what happens around the selection. Or worse, they don’t support tried and true techniques like cross-validation.
This whitepaper discusses the four mandatory components for the correct validation of machine learning models, and how correct model validation works inside RapidMiner Studio.
