- Linear Regression
The most basic version of linear regression is Simple Linear Regression. This is a method used for predicting a particular "response" using a single "feature".
For a given dataset, a feature is an independent variable and the response is the dependent variable. For example, if our dataset comprised of the velocity of a vehicle over time, then time would be the feature and velocity would be the response.
In Linear Regression, it is assumed that the two variables have a linear relationship (hence the name). The object of this method is to find a linear function, that predicts the response value for a particular feature value as accurately as possible.
If the various points are plotted on a scatter plot, the problem of Linear Regression basically asks us: what is the line that best fits the above scatter plot, by minimising the inaccuraccy.