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Crossvalidation helps assess the model's generalization ability from "summary" of Machine Learning For Dummies by John Paul Mueller,Luca Massaron
Crossvalidation is a technique used to assess how well a machine learning model can generalize to new, unseen data. When you build a machine learning model, you want it to not only perform well on the training data but also on new data that it hasn't seen before. Crossvalidation helps you estimate how well your model will generalize by simulating the process of training and testing on different subsets of the data. The basic idea behind crossvalidation is to split your data into multiple subsets, or folds. You then train your model on a subset of the data and test it on a different subset. By repeating this process multiple times with different subsets, you can get a more robust estimate of how well your model will perform on ...
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