How else can you get rid of the minus sign? We could square our differences, that is, we take our predictions, subtract the true values ​​from them and square them, thereby obtaining the squared deviation. Now we can sum all the squared deviations and divide by their number of objects. This value shows how much our predictions deviate from the truth squared. It is called **the mean square error or MSE**. When the MSE goes to zero, in this case, all true values ​​match our predictions perfectly.

 Or just use the similar to the previous chapter method:

from sklearn.metrics import mean_squared_error
print(mean_squared_error(Y_test, y_test_predicted))

But there is a small snag here. It is important to note that when calculating the error, the MSE units do not match the original units of the predicted target value. We predict the number of phenols squared, which is sometimes a bit difficult to use for estimation. You can get rid of this square quite simply by taking the square root of MSE. It calls **the root mean squared error (RMSE)**. Thus, it may be common to use the MSE loss to train a regression prediction model and use the RMSE to evaluate and report its performance.

Ideally, all the metrics that we considered (MSE, RMSE, MAE) tend to **zero** when all true values are very close to predictions. But these metrics do not have a right border above which we consider metrics to be useless. Is our prediction good if MSE=13 or not? 

Let’s look at an example:

In both cases **MSE = 13**. For the first table, this is a very large value. We are more than twice wrong in our forecasts. At the same time, for the second sample, this is a fairly good estimate.

Regression is one of the machine learning algorithms. In this course you will learn how to work with data, build the model and make predictions about the future values. 

Basics of the linear regression concept using Python.

What the correlation is and how it works in data science.

Deepening into programming, learn how to build and train your own linear regression model.

To understand how good the model is, it needs to be evaluated. In this section, we will consider the metrics for evaluating the built models and figuring out how to work with them in Python.

Here we will learn the technique to use several variables to predict the future outcome.

MSE and RMSE

Solución

Awesome!

MSE and RMSE

Solución

Awesome!