gaspersic/freeCodeCamp
1
0
Fork 0
Code Issues Pull Requests Projects Releases Wiki Activity

Updated the definition of Linear Regression (#21908)

Browse Source 
* Updated the definition of Linear Regression

Stated that linear regression is a supervised and discriminative machine learning algorithm.

* Added the link to a descriptive article
This commit is contained in:
Mudit Choraria
2018-11-17 23:47:07 +05:30
committed by Christopher McCormack
parent 76f416935b
commit 809c83049d
1 changed files with 3 additions and 0 deletions
Download Patch File Download Diff File Expand all files Collapse all files

3
guide/english/machine-learning/linear-regression/index.md
View File

@ -2,6 +2,9 @@
title: Linear Regression title: Linear Regression
--- ---
## Linear Regression ## Linear Regression
[Linear Regression](https://masmlblog.wordpress.com/2017/10/31/the-linear-regression-algorithm/) is a **discriminative** machine learning algorithm to relate the dependent variable, i.e ‘Y’ on the independent variables i.e ‘Xi‘. Unlike generative models, it does not allow the user to generate samples from the distribution of X and Y. Linear regression is a **supervised** learning algorithm, i.e labelled dataset is needed to train it for the model to be able to make predictions on its own.
Linear regression is a type of regression, or one of the several regression techniques which are used to find the best fitting line for the given set of points in the given dataset. Linear regression is a type of regression, or one of the several regression techniques which are used to find the best fitting line for the given set of points in the given dataset.
Linear regression helps us predict score of a variable X from the scores on other variables Y. When the variables Y are plotted, linear regression finds the best-fitting straight line through the points. The best-fitting line is called a regression line. Linear regression helps us predict score of a variable X from the scores on other variables Y. When the variables Y are plotted, linear regression finds the best-fitting straight line through the points. The best-fitting line is called a regression line.
This is done by taking a line equation and comparing it with the points and the required result and then calibrated in such a way that the difference/distance between the points and the line, or simply error, is kept to the minimum. This way of calibrating is called Least Squared Error method. This is done by taking a line equation and comparing it with the points and the required result and then calibrated in such a way that the difference/distance between the points and the line, or simply error, is kept to the minimum. This way of calibrating is called Least Squared Error method.