Valutare l'errore di training

Ora valuterai la RMSE sul training set ottenuta dall'albero di regressione dt che hai istanziato in un esercizio precedente.

Oltre a dt, nel tuo workspace sono disponibili X_train e y_train.

Nota che in scikit-learn, la MSE di un modello può essere calcolata così:

MSE_model = mean_squared_error(y_true, y_predicted)

dove usiamo la funzione mean_squared_error dal modulo metrics e le passiamo come primo argomento le etichette vere y_true, e come secondo argomento le etichette predette dal modello y_predicted.

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Machine Learning con modelli ad alberi in Python

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Istruzioni dell'esercizio

Importa mean_squared_error come MSE da sklearn.metrics.
Allena dt sul training set.
Predici le etichette del training set con dt e assegna il risultato a y_pred_train.
Valuta la RMSE sul training set di dt e assegnala a RMSE_train.

Esercizio pratico interattivo

Prova a risolvere questo esercizio completando il codice di esempio.

# Import mean_squared_error from sklearn.metrics as MSE
____

# Fit dt to the training set
____.____(____, ____)

# Predict the labels of the training set
____ = ____.____(____)

# Evaluate the training set RMSE of dt
____ = (____(____, ____))**(___)

# Print RMSE_train
print('Train RMSE: {:.2f}'.format(RMSE_train))

Modifica ed esegui il codice

Questo esercizio fa parte del corso

Machine Learning con modelli ad alberi in Python

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Esercizio in corso

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