Sveip for å vise menyen

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.

Oppgave

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

Bytt til skrivebordet for virkelighetspraksisFortsett der du er med et av alternativene nedenfor

Alt var klart?

Takk for tilbakemeldingene dine!

Seksjon 5. Kapittel 2

Spør AI

Spør om hva du vil, eller prøv ett av de foreslåtte spørsmålene for å starte chatten vår

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

Oppgave

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

Bytt til skrivebordet for virkelighetspraksisFortsett der du er med et av alternativene nedenfor

Alt var klart?

Takk for tilbakemeldingene dine!

Seksjon 5. Kapittel 2

Bytt til skrivebordet for virkelighetspraksisFortsett der du er med et av alternativene nedenfor