I explore how classifiers can handle increasing feature space dimension. I simulate three classes using the make_classification() function from sklearn with different numbers of features. We begin with n > p and end with p > n. We explore what happens to the accuracy as p increases.
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I simulate data to show the curse of dimensionality for classification.
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