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Crossvalidation helps prevent overfitting by testing the model on multiple subsets of the data from "summary" of Data Science for Business by Foster Provost,Tom Fawcett
Crossvalidation is an important technique in data science that helps prevent overfitting. Overfitting occurs when a model learns the noise in the training data rather than the underlying pattern. This can lead to poor performance on new, unseen data. Crossvalidation helps address this issue by testing the model on multiple subsets of the data. By splitting the data into multiple subsets or folds, crossvalidation allows the model to be trained on one subset and tested on another. This process is repeated multiple times, with each subset serving as both training and testing data. This way, the model is evaluated on different portions of the data, which helps to ensure that it generalizes well to unseen data. Crossvalidation provides a more reliable estimate of how well the model will perform on new data compared to simply training and testing on a single split of the data. It helps to reduce the risk of overfitting by assessing the model's performance on different subsets of the data.- Crossvalidation is a valuable tool in the data scientist's toolkit for building robust and generalizable models. It helps to ensure that the model is not just memorizing the training data but is actually learning the underlying patterns that will allow it to make accurate predictions on new data. By testing the model on multiple subsets of the data, crossvalidation helps to prevent overfitting and improve the model's performance.
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