Crossvalidation assesses the performance of machine learning models from "summary" of Data Science For Dummies by Lillian Pierson
Crossvalidation is a crucial technique in the data scientist's toolbox. It allows you to assess how well your machine learning models are performing before you deploy them in the real world. This validation process is essential because it gives you insights into how your models will perform on unseen data. Imagine you have a dataset that you've split into a training set and a testing set. You train your model on the training set and then evaluate its performance on the testing set. While this approach can give you a good idea of how well your model is doing, it may not provide a complete picture. Crossvalidation addresses this limitation by dividing your dataset into multiple subsets or folds. It then trains your model on a combination of these folds a...
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