PCA
0 antonyms and opposites of PCA, grouped by meaning.
Noun — Principal component analysis: a statistical technique that reduces a large set of correlated variables to a smaller set of uncorrelated components that still capture most of the variation in the data.
We haven't recorded synonyms for PCA yet. You can still read its full definition, where it comes from, or words that rhyme with it.
How to Use Pca
The word patterns that sound natural to a native speaker.
Always define the expansion on first use in writing, since the three main meanings sit in very different fields.
Etymology
An initialism, expanded differently in different fields.
Full origin of PCA →Frequently Asked Questions
What is the difference between PCA and factor analysis?
PCA is descriptive: it repackages the observed variance into components with no assumption about what causes it. Factor analysis is a model: it assumes hidden latent factors generate the observed variables and estimates those factors. They often give similar-looking output but answer different questions.
What does a PCA pump do?
It delivers a fixed small dose of painkiller when the patient presses a handset, then locks out further doses for a set period so an overdose is not possible. Staff programme the dose size, lockout interval and total hourly limit.
How many principal components should you keep?
There is no single rule. Common approaches are keeping components that together explain a target share of variance, keeping those with eigenvalues above 1, or reading the elbow on a scree plot. The choice should also be guided by whether the retained components are interpretable.