How does mean squared error help in evaluating model accuracy?
In the realm of data science, the accuracy of predictive models is paramount. One of the key tools you have at your disposal for evaluating a model's performance is the mean squared error (MSE). Essentially, MSE measures the average of the squares of the errors—that is, the average squared difference between the estimated values and the actual value. This metric can provide you with insight into the quality of a model, allowing you to fine-tune it for better accuracy. By understanding how MSE reflects model performance, you can make more informed decisions in your data science projects.