FAQ: Multiple Linear Regression - Evaluating the Model's Accuracy

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This community-built FAQ covers the “Evaluating the Model’s Accuracy” exercise from the lesson “Multiple Linear Regression”.

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This exercise can be found in the following Codecademy content:

Machine Learning

FAQs on the exercise Evaluating the Model’s Accuracy

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2

Are we supposed to rely on the mean squared error regression loss for the testing set or the training set when evaluating the effectiveness of our model?

1 Like
3

Can anyone please explain how LinearRegression().score works?

How does it know which y-outputs to compare to calculate the R² coefficient?

In this exercise we provide (x_train, y_train) and (x_test, y_test) as inputs to obtain the mean squared error regression loss for the training set and the the testing set, respectively:

print(model.score(x_train, y_train))
print(model.score(x_test, y_test))

How does it know what the predicted y-values are?

4

The model basically factors in (substitutes into its equation) x_train to compute predicted values from the data and then compare with y_train to get the residual error, which is then successively squared and summed up; finally divided by the sum of total squares.

5

Yeah same I was confused as to where are the y_predict in the .score() method!
As @kennyudekwu227012157 was saying it seems that:

  • you feed .score() with y_test and x_test
  • it uses those values in the background to compute the y_predict (with the .fit() methods)
  • it now possesses the y_test and y_predict to properly calculate R²

that’s how I understand it