Imposta la griglia di iperparametri di RF

In questo esercizio imposterai manualmente la griglia di iperparametri che verrà usata per ottimizzare gli iperparametri di rf e trovare il regressore ottimale. A questo scopo, costruirai una griglia di iperparametri e regolerai il numero di stimatori, il numero massimo di feature usate quando si divide ciascun nodo e il numero minimo di campioni (o frazione) per foglia.

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Machine Learning con modelli ad alberi in Python

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Istruzioni dell'esercizio

Definisci una griglia di iperparametri corrispondente a un dizionario Python chiamato params_rf con:
- la chiave 'n_estimators' impostata a una lista di valori 100, 350, 500
- la chiave 'max_features' impostata a una lista di valori 'log2', 'auto', 'sqrt'
- la chiave 'min_samples_leaf' impostata a una lista di valori 2, 10, 30

Esercizio pratico interattivo

Prova a risolvere questo esercizio completando il codice di esempio.

# Define the dictionary 'params_rf'
params_rf = ____

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Machine Learning con modelli ad alberi in Python

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Esercizio in corso

Exercise 9: Cerca la foresta ottimale Exercise 10: Valuta la foresta ottimale Exercise 11: Congratulazioni!