/Linear-Regression-Extensive-Adventure

Primary LanguageJupyter Notebook

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Linear Regression - An Extensive Adventure

There are two very different solution approaches for the Linear Regression problem.

  • The “closed-form” solution approach known as the Ordinary Least Squares (OLS) method.
  • Iterative optimization approach known as Gradient Descent (GD).

We will perform an extensive investigation of these two approaches using Scikit-Learn in a series of four notebooks. For this exploration we will use the Boston Housing dataset that has 506 samples and 13 features.

Index for the Notebook Series on Scikit-Learn Solutions for Linear Regression

There are four notebooks on sklearn Linear Regression.

  1. Linear Regression-1-OLS -- OLS method & Regularized OLS Method (Ridge Rergression)
  2. Linear Regression-2-OLS Polynomial Regression-Frequentist Approach (MLE) -- Polynomial regression using the OLS method
  3. Linear Regression-3-OLS Polynomial Regression-Bayesian Approach (MAP) -- Polynomial regression using the regularized OLS method
  4. Linear Regression-4-Gradient Descent -- Iterative optimization approach (Gradient Descent & Stochastic Gradient Descent)