Answer Posted / Peeyush Tripathi

The curse of dimensionality refers to the negative effects high-dimensional spaces have on machine learning algorithms' performance and efficiency. As the number of features increases, data becomes sparse, leading to increased computational cost, poor generalization, and overfitting. To combat the curse of dimensionality, techniques such as feature selection, dimensionality reduction (PCA, t-SNE), and kernel methods can be used.

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