In machine learning, the backward elimination method is used to select the best subset of characteristics from a given set of features. It operates by repeatedly eliminating features with low or no predictive value for the target variable.
Fitting a multivariate linear regression model incorporating each of the independent variables is the first step in the backward elimination procedure. A new model fits once the variable with the greatest p-value is eliminated from the original one.
The uplifting development of Artificial Intelligence and Machine learning has introduced many new technologies for a well shaped society. The Backward Elimination is also one of them. It is one of the method of machine learning part to find the best subset of features from a given set of features. This technique made possible to reduce the chances of overfitting the data and make the linear regression model more interpretable. It works by iteratively removing features that are not predictive of the target variable or have the least predictive power. 