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Results tagged with pca
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user 30595
Principal component analysis (PCA) is a linear dimensionality reduction technique. It reduces a multivariate dataset to a smaller set of constructed variables preserving as much information (as much variance) as possible. These variables, called principal components, are linear combinations of the input variables.
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Interpretation of PCA Report from Machine Learning Toolkit
I have been using the PCA widget for dimensionality reduction and I want to investigate the relationship of PCA components to the original variables. … If so, is there a simple way to determine which they are form the PCA results? …
- 109
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Accepted
Interpretation of PCA Report from Machine Learning Toolkit
Okay, the following explains most of the PCA report. So I am a step closer to digging down into the data. … I found this website: Interpreting Results from PCA Which states:
The principal component variables are defined as linear combinations
of the original variables X1,...,Xk,...,Xm. …
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