Experimental Analysis of Methods Used to Solve Linear Regression Models

Predicting the value of one or more variables using the values of other variables is a very important process in the various engineering experiments that include large data that are difficult to obtain using different measurement processes. Regression is one of the most important types of supervised machine learning, in which labeled data is used to build a prediction model, regression can be classified into three different categories: linear, polynomial, and logistic. In this research paper, different methods will be implemented to solve the linear regression problem, where there is a linear relationship between the target and the predicted output. Various methods for linear regression will be analyzed using the calculated Mean Square Error (MSE) between the target values and the predicted outputs. A huge set of regression samples will be used to construct the training dataset with selected sizes. A detailed comparison will be performed between three methods, including least-square fit; Feed-Forward Artificial Neural Network (FFANN), and Cascade Feed-Forward Artificial Neural Network (CFFANN), and recommendations will be raised. The proposed method has been tested in this research on random data samples, and the results were compared with the results of the most common method, which is the linear multiple regression method. It should be noted here that the procedures for building and testing the neural network will remain constant even if another sample of data is used.