Intro to Regularization With Ridge And Lasso Regression with Sklearn

By Medium - 2021-02-28

Description

Linear Regression a.k.a. Ordinary Least Squares is one of the easiest and most widely used ML algorithms. But it suffers from a fatal flaw — it is super easy for the algorithm to overfit the training…

Summary

  • You might as well ditch Linear Regression Problems of Linear Regression Linear Regression a.k.a.
  • With large coefficients, it is easy to predict nearly everything — you just take the relevant combination of individual slopes (βs) and you get the answer.
  • Ideally, the perfect model would have low bias and low variance but that is easier said than done.
  • Apart from OLS (the first part), ridge regression squares every individual slope of the feature variables and scales them by some number 𝜆.
  • This is called the Ridge Regression penalty.

 

Topics

  1. Machine_Learning (0.2)
  2. Backend (0.1)
  3. NLP (0.05)

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