Valutare le prestazioni del Bagging

Ora che hai istanziato il classificatore bagging, è il momento di addestrarlo e valutarne l'accuratezza sul set di test.

Il dataset Indian Liver Patient è già stato preprocessato e suddiviso in 80% train e 20% test. Le matrici delle caratteristiche X_train e X_test, così come gli array delle etichette y_train e y_test, sono disponibili nel tuo workspace. Inoltre, abbiamo già caricato il classificatore bagging bc che hai 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

Addestra bc sul training set.
Prevedi le etichette del test set e assegna il risultato a y_pred.
Determina l'accuratezza sul test set di 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 acc_test
acc_test = ____(____, ____)
print('Test set accuracy of bc: {:.2f}'.format(acc_test))

Modifica ed esegui il codice

Questo esercizio fa parte del corso

Machine Learning con modelli ad alberi in Python

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Exercise 1: Bagging Exercise 2: Definisci il classificatore bagging Exercise 3: Valutare le prestazioni del Bagging

Esercizio in corso

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Exercise 1: Adaboost Exercise 2: Define the AdaBoost classifier Exercise 3: Train the AdaBoost classifier Exercise 4: Evaluate the AdaBoost classifier Exercise 5: Gradient Boosting (GB)Exercise 6: Define the GB regressor Exercise 7: Train the GB regressor Exercise 8: Evaluate the GB regressor Exercise 9: Stochastic Gradient Boosting (SGB)Exercise 10: Regression with SGB Exercise 11: Train the SGB regressor Exercise 12: Evaluate the SGB regressor

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