Punteggio OOB vs punteggio sul test set

Ora che hai istanziato bc, lo adatterai al training set e valuterai le accuratezze sul test set e OOB.

Il dataset è già stato preparato e suddiviso in 80% train e 20% test. Le matrici delle caratteristiche X_train e X_test, così come gli array di etichette y_train e y_test, sono disponibili nel tuo workspace. Inoltre, abbiamo anche caricato il classificatore bc istanziato nell'esercizio precedente e la funzione accuracy_score() da sklearn.metrics.

Questo esercizio fa parte del corso

Machine Learning con modelli ad alberi in Python

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Istruzioni dell'esercizio

Adatta bc al training set, predici le etichette del test set e assegna i risultati a y_pred.
Valuta l'accuratezza sul test set acc_test chiamando accuracy_score.
Valuta l'accuratezza OOB di bc, acc_oob, estraendo l'attributo oob_score_ da bc.

Esercizio pratico interattivo

Prova a risolvere questo esercizio completando il codice di esempio.

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

# Predict test set labels
y_pred = ____.____(____)

# Evaluate test set accuracy
acc_test = ____(____, ____)

# Evaluate OOB accuracy
acc_oob = ____.____

# Print acc_test and acc_oob
print('Test set accuracy: {:.3f}, OOB accuracy: {:.3f}'.format(acc_test, acc_oob))

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Questo esercizio fa parte del corso

Machine Learning con modelli ad alberi in Python

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