Definisci il classificatore AdaBoost

Nei prossimi esercizi riprenderai il dataset Indian Liver Patient, già introdotto in un capitolo precedente. Il tuo compito è prevedere se un paziente soffre di una malattia del fegato usando 10 feature, tra cui Albumina, età e genere. Questa volta, però, allenerai un ensemble AdaBoost per svolgere il compito di classificazione. Inoltre, dato che questo insieme di dati è sbilanciato, userai la metrica ROC AUC al posto dell'accuratezza.

Come primo passo, inizia istanziando un classificatore AdaBoost.

Questo esercizio fa parte del corso

Machine Learning con modelli ad alberi in Python

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Istruzioni dell'esercizio

Importa AdaBoostClassifier da sklearn.ensemble.
Istanzia un DecisionTreeClassifier con max_depth impostato a 2.
Istanzia un AdaBoostClassifier composto da 180 alberi e imposta base_estimator a dt.

Esercizio pratico interattivo

Prova a risolvere questo esercizio completando il codice di esempio.

# Import DecisionTreeClassifier
from sklearn.tree import DecisionTreeClassifier

# Import AdaBoostClassifier
____

# Instantiate dt
dt = ____(____=____, random_state=1)

# Instantiate ada
ada = ____(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: 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: Definisci il classificatore AdaBoost

Esercizio in corso

Exercise 3: Allena il classificatore AdaBoost Exercise 4: Valuta il classificatore AdaBoost Exercise 5: Gradient Boosting (GB)Exercise 6: Definisci il regressore GB Exercise 7: Allena il regressore GB Exercise 8: Valuta il regressore GB Exercise 9: Stochastic Gradient Boosting (SGB)Exercise 10: Regressione con SGB Exercise 11: Allena il regressore SGB Exercise 12: Valuta il regressore SGB

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Exercise 1: Tuning a CART's Hyperparameters Exercise 2: Tree hyperparameters Exercise 3: Set the tree's hyperparameter grid Exercise 4: Search for the optimal tree Exercise 5: Evaluate the optimal tree Exercise 6: Tuning a RF's Hyperparameters Exercise 7: Random forests hyperparameters Exercise 8: Set the hyperparameter grid of RF Exercise 9: Search for the optimal forest Exercise 10: Evaluate the optimal forest Exercise 11: Congratulations!