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Try to Evaluate

Let’s see which model is better using the metrics we already know.

MSE:


              123
            
from sklearn.metrics import mean_squared_error
print(mean_squared_error(Y_test, y_test_predicted).round(2))
print(mean_squared_error(Y_test, y_test_predicted2).round(2))


python

MAE:


              123
            
from sklearn.metrics import mean_absolute_error
print(mean_absolute_error(Y_test, y_test_predicted).round(2))
print(mean_absolute_error(Y_test, y_test_predicted2).round(2))


python

R-squared:


              123
            
from sklearn.metrics import r2_score
print(r2_score(Y_test, y_test_predicted).round(2))
print(r2_score(Y_test, y_test_predicted2).round(2))


python

As a general rule, the more features a model includes, the lower the MSE (RMSE) and MAE will be. However, be careful about including too many features. Some of them may be extremely random, degrading the model's interpretability.

Opgave

Swipe to start coding

Let’s evaluate the model from the previous task:

[Line #30] Import mean_squared_error for calculating metrics from scikit.metrics.
[Line #31] Find MSE using method mean_squared_error() and Y_test, y_test_predicted2 as the parameters, assign it to the variable MSE, round the result to second digit.
[Line #32] Print the variable MSE.
[Line #35] Import r2_score from scikit.metrics.
[Line #36] Find R-squared and assign it to the variable r_squared, round the result to second digit.
[Line #37] Print the variable r_squared.

Løsning

Skift til skrivebord for at øve i den virkelige verdenFortsæt der, hvor du er, med en af nedenstående muligheder

Var alt klart?

Tak for dine kommentarer!

Sektion 5. Kapitel 2

Spørg AI

Spørg om hvad som helst eller prøv et af de foreslåede spørgsmål for at starte vores chat

Try to Evaluate

Let’s see which model is better using the metrics we already know.

MSE:


              123
            
from sklearn.metrics import mean_squared_error
print(mean_squared_error(Y_test, y_test_predicted).round(2))
print(mean_squared_error(Y_test, y_test_predicted2).round(2))


python

MAE:


              123
            
from sklearn.metrics import mean_absolute_error
print(mean_absolute_error(Y_test, y_test_predicted).round(2))
print(mean_absolute_error(Y_test, y_test_predicted2).round(2))


python

R-squared:


              123
            
from sklearn.metrics import r2_score
print(r2_score(Y_test, y_test_predicted).round(2))
print(r2_score(Y_test, y_test_predicted2).round(2))


python

Opgave

Swipe to start coding

Let’s evaluate the model from the previous task:

[Line #30] Import mean_squared_error for calculating metrics from scikit.metrics.
[Line #31] Find MSE using method mean_squared_error() and Y_test, y_test_predicted2 as the parameters, assign it to the variable MSE, round the result to second digit.
[Line #32] Print the variable MSE.
[Line #35] Import r2_score from scikit.metrics.
[Line #36] Find R-squared and assign it to the variable r_squared, round the result to second digit.
[Line #37] Print the variable r_squared.

Løsning

Skift til skrivebord for at øve i den virkelige verdenFortsæt der, hvor du er, med en af nedenstående muligheder

Var alt klart?

Tak for dine kommentarer!

Sektion 5. Kapitel 2

Skift til skrivebord for at øve i den virkelige verdenFortsæt der, hvor du er, med en af nedenstående muligheder