Pca Dimensionality Reduction | Machine Learning

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easyPca Dimensionality Reduction

PCA is applied to a dataset with 50 features. The first principal component explains 45% of variance, the second explains 20%, and the third explains 15%. A data scientist says "the first three components capture 80% of the variance, so we can safely discard the remaining 47 components." What important nuance does this claim miss?

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easyPca Dimensionality Reduction

PCA is applied to a dataset with 50 features. The first principal component explains 45% of variance, the second explains 20%, and the third explains 15%. A data scientist says "the first three components capture 80% of the variance, so we can safely discard the remaining 47 components." What important nuance does this claim miss?