If after applying PCA to a dataset, you find that only the first few principal components capture most of the variance, what does this imply

Question

Accepted Answer

Most of the information in the data is concentrated in a few components, so dimensionality reduction is effective

Answer

The data is not linearly separable, and PCA is not useful.

Answer

The dataset has a large number of irrelevant features.

Answer

The data contains too many missing values for PCA to be applied effectively.

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