Data Science, Linear Regression, Machine Learning, Python, Numpy.
between predicted and actual values. The most common cost function used in linear regression is the mean squared error (MSE). MSE calculates the average of the squared differences between the predicted and actual values for each observation in the data set. This gives you an overall measure of how