Answer / Surya Prakash

When dealing with high-dimensional data (p > n), techniques like regularization, feature selection, and dimensionality reduction become important. Regularization helps prevent overfitting by adding a penalty term to the loss function, feature selection helps select only the most relevant features, and dimensionality reduction techniques like PCA or t-SNE help reduce the number of dimensions while retaining as much information as possible.

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