Definisci il classificatore bagging

Nei prossimi esercizi lavorerai con il dataset Indian Liver Patient dell’UCI machine learning repository. Il tuo compito è prevedere se un paziente soffre di una malattia del fegato usando 10 caratteristiche, tra cui Albumina, età e genere. Lo farai utilizzando un Bagging Classifier.

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Machine Learning con modelli ad alberi in Python

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Istruzioni dell'esercizio

Importa DecisionTreeClassifier da sklearn.tree e BaggingClassifier da sklearn.ensemble.
Istanzia un DecisionTreeClassifier chiamato dt.
Istanzia un BaggingClassifier chiamato bc composto da 50 alberi.

Esercizio pratico interattivo

Prova a risolvere questo esercizio completando il codice di esempio.

# Import DecisionTreeClassifier
____

# Import BaggingClassifier
____

# Instantiate dt
dt = ____(random_state=1)

# Instantiate bc
bc = ____(base_estimator=____, n_estimators=____, random_state=1)

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

Esercizio in corso

Exercise 3: Valutare le prestazioni del Bagging Exercise 4: Valutazione Out-of-Bag Exercise 5: Prepara il terreno Exercise 6: Punteggio OOB vs punteggio sul test set Exercise 7: Random Forests (RF)Exercise 8: Allena un regressore RF Exercise 9: Valuta il regressore RF Exercise 10: Visualizzare l'importanza delle feature

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