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# Unraveling the Efficacy of LASSO: A Comprehensive Guide to Least Absolute Shrinkage and Selection Operator in Regression Analysis

**[Python in Plain English](https://daily.dev/sources/inPlainEngHQ)** · 4 min read · 0 upvotes · 0 comments

## Summary

LASSO is a regression analysis method that performs variable selection and regularization, improving prediction accuracy and interpretability. It is widely used in data science for high-dimensional data and has applications in finance, genomics, and marketing. However, it has limitations in selecting variables from highly correlated groups and when the number of predictors exceeds the number of observations.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://python.plainenglish.io/unraveling-the-efficacy-of-lasso-a-comprehensive-guide-to-least-absolute-shrinkage-and-selection-e30215120cda>

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---

Tags: [#data-science](https://daily.dev/tags/data-science), [#statistics](https://daily.dev/tags/statistics), [#regression-analysis](https://daily.dev/tags/regression-analysis)

[View this post on daily.dev](https://daily.dev/posts/unraveling-the-efficacy-of-lasso-a-comprehensive-guide-to-least-absolute-shrinkage-and-selection-op-sokyf4tm0)

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