The PCA (principal component analysis) contribution ratio is a measure of how much each principal component contributes to the variance of the overall data. Specifically, it is calculated by dividing the eigenvalue of each principal component by the sum of all eigenvalues. The higher the contribution ratio, the better the principal component captures the characteristics of the data.
Wikipedia, for example.決定係数の別名みたいに書かれてるが違和感、別物だよね
The coefficient of determination (R2) is a value in statistics that indicates how much of the dependent variable (objective variable) is explained by the independent variable (explanatory variable). It is also sometimes called the contribution ratio. It is used as a measure of the goodness of fit of a regression equation (model) obtained from sample values. --- coefficient of determination - Wikipedia
The coefficient of determination (R²) is a measure of how well a model explains data in regression analysis. PCA is used for dimensionality reduction and to understand the structure of the data.
PCA’s “contribution ratio” has the following aliases
- Japanese:
- Contribution rate
- [Explanatory variance: Used to emphasize how well each principal component explains the variance in the data.
- English:
- Explained Variance: A measure of how well each principal component explains the variance in the data.
- Proportion of Variance Explained (PVE): An expression indicating the ratio of the variance of each principal component to the overall variance.
- Variance Contribution: A literal translation of “contribution ratio,” which is not very common but is sometimes used with the same meaning.
- Explained Variance” is particularly commonly used.
Explained variation - Wikipedia
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In statistics, explained variation measures the proportion to which a mathematical model accounts for the variation (dispersion) of a given data set. Often, variation is quantified as variance; then, the more specific term explained variance can be used.
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