Why is Variance important in principal component analysis?

Question

Accepted Answer

Variance in principal component analysis helps to identify the most important features in the data.

Variance measures the spread of data points around the mean, indicating the importance of each feature in capturing the overall variability.
Higher variance implies more information is retained by the principal components, making them more significant in representing the data.
By selecting components with high variance, we can reduce the dimensionality of the data while preserving as much information as possible.
Variance is used to rank the principal components in PCA, with the most variance explaining components retained for analysis.

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