Prestazioni migliori con un Voting Classifier

Infine, valuterai le prestazioni di un voting classifier che prende gli output dei modelli definiti nella lista classifiers e assegna le etichette tramite voto di maggioranza.

X_train, X_test, y_train, y_test, la lista classifiers definita in un esercizio precedente, così come la funzione accuracy_score da sklearn.metrics, sono disponibili nel tuo workspace.

Questo esercizio fa parte del corso

Machine Learning con modelli ad alberi in Python

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Istruzioni dell'esercizio

Importa VotingClassifier da sklearn.ensemble.
Istanzia un VotingClassifier impostando il parametro estimators a classifiers e assegnalo a vc.
Esegui il fit di vc sul training set.
Valuta l'accuratezza di vc sul test set usando le predizioni sul test set y_pred.

Esercizio pratico interattivo

Prova a risolvere questo esercizio completando il codice di esempio.

# Import VotingClassifier from sklearn.ensemble
____

# Instantiate a VotingClassifier vc
vc = ____(estimators=____)     

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

# Evaluate the test set predictions
y_pred = vc.predict(X_test)

# Calculate accuracy score
accuracy = ____(____, ____)
print('Voting Classifier: {:.3f}'.format(accuracy))

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: Errore di generalizzazione Exercise 2: Complessità, bias e varianza Exercise 3: Overfitting e underfitting Exercise 4: Diagnostica i problemi di bias e varianza Exercise 5: Istanzia il modello Exercise 6: Valuta l'errore di CV a 10 fold Exercise 7: Valutare l'errore di training Exercise 8: Bias alto o varianza alta?Exercise 9: Ensemble Learning Exercise 10: Definisci l'ensemble Exercise 11: Valuta i singoli classificatori Exercise 12: Prestazioni migliori con un Voting Classifier

Esercizio in corso

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Exercise 1: Bagging Exercise 2: Define the bagging classifier Exercise 3: Evaluate Bagging performance Exercise 4: Out of Bag Evaluation Exercise 5: Prepare the ground Exercise 6: OOB Score vs Test Set Score Exercise 7: Random Forests (RF)Exercise 8: Train an RF regressor Exercise 9: Evaluate the RF regressor Exercise 10: Visualizing features importances

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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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