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What is the curse of dimensionality?

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As the number of features grows, data becomes sparse in the space and distances between points become similar, so methods that rely on neighbourhoods, like k-nearest neighbours, degrade. You need far more data to cover the space, which is why feature selection, regularisation and dimensionality reduction matter.

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