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 and evaluates its performance on the remaining folds. This process is repeated multiple times, with each fold serving as both a training set and a testing set.
By averaging the model's performance across all folds, you get a more robust estimate of how well it will generalize to new data. This is particularly important when working with small datasets or when there is a risk of overfitting.
Crossvalidation helps you assess the variability in your model's performance and identify any potential issues that need to be addressed. It also allows you to compare different models and select the one that performs best on unseen data. In this way, crossvalidation plays a critical role in ensuring the reliability and effectiveness of your machine learning models.
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