Normal Equation

In machine learning, various optimization techniques can be used to reduce the error and thus increase the accuracy rate. In this article, we will discuss the optimization of machine learning models with the normal equation method, and in order to understand this, we should first take a look at the concept of the cost function. The cost function, although it has different variations (see MAE, RMSE, MSE), basically contains 2 variables (y_real, y_predicted); It allows us to measure the error, in other words, the difference between the actual output values and the predicted output values in machine learning models. As can be seen in the figure, the sum of the squares of the differences between the y values estimated as a result of the hypothesis function and the actual y values gives us the root squared cost function. If you want to learn more about this concept, you can reach my article on this subject from the link below.