Linear Regression: A Technical Overview

BigML has added multiple linear regression to its suite of supervised learning methods. In this sixth and final blog post of our series, we will give a rundown of the technical details for this method.

Model Definition

Given a numeric objective field $y$ we model its response as a linear combination of our inputs $x_1,\cdots,x_n$ and an intercept value $\beta_0$

$y = \beta_0 + \beta_1 x_1 + \beta_2 x_2 + \ldots + \beta_n x_n = \beta_0 + \sum_{i=1}^n \beta_i x_i$

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