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Put another way, the COD states that as we increase the number of feature columns, we need exponentially more data to maintain the same level of model performance. This is because, in high-dimensional spaces, even the nearest neighbors can be very far away from a given data point, making it difficult to create good predictions. High dimensionality also increases the risk of overfitting as the model may start to fit to noise in the data rather than the actual signal.