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  "Author": "Michael Hernandez <micahel@hernandez.ai>, Yuri Balasanov",
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  "Description": "Performs Principal Components Analysis (also known as PCA)\ndimensionality reduction in the context of a linear regression.\nIn most cases, PCA dimensionality reduction is performed\nindependent of the response variable for a regression. This\ncaptures the majority of the variance of the model's\npredictors, but may not actually be the optimal dimensionality\nreduction solution for a regression against the response\nvariable. An alternative method, optimized for a regression\nagainst the response variable, is to use both PCA and a\nrelative importance measure. This package applies PCA to a\ngiven data frame of predictors, and then calculates the\nrelative importance of each PCA factor against the response\nvariable. It outputs ordered factors that are optimized for\nmodel fit. By performing dimensionality reduction with this\nmethod, an individual can achieve a the same r-squared value as\nperforming just PCA, but with fewer PCA factors. References:\nYuri Balasanov (2017) <https://ilykei.com>.",
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