Work out pca components for variance instantly with clear inputs, formula shown and shareable results.
PCA orders components by the variance each explains, so retaining a target share of variance is a cumulative-sum problem: add the ratios in order until they cross the threshold. The count where that happens is the dimensionality you can reduce to, and the components dropped are the directions along which the data barely varies — often noise rather than signal.
Cumulative variance
k = smallest k such that (sum of the first k explained variance ratios) / total >= target
Yes, unless every feature already shares a unit. PCA maximises variance, so an unscaled feature measured in thousands will dominate the first component regardless of its relevance.
It is a convention, not a rule. For visualisation two or three components suffice; for a downstream model, tune the component count against validation performance instead.