![]() ![]() 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. ![]() The best-fitting line is called a regression line. When the variables Y are plotted, linear regression finds the best-fitting straight line through the points. Linear regression helps us predict score of a variable X from the scores on other variables Y. 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 supervised learning algorithm, i.e labelled dataset is needed to train it for the model to be able to make predictions on its own. Unlike generative models, it does not allow the user to generate samples from the distribution of X and Y. ![]() Linear Regression is a discriminative machine learning algorithm to relate the dependent variable, i.e ‘Y’ on the independent variables i.e ‘Xi‘. Title: Linear Regression Linear Regression
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