Entropia vs indice di Gini

In questo esercizio confronterai l'accuratezza sul set di test di dt_entropy con quella di un altro albero chiamato dt_gini. L'albero dt_gini è stato addestrato sullo stesso insieme di dati con gli stessi parametri, tranne che per il criterio d'informazione, impostato sull'indice di Gini usando la parola chiave 'gini'.

X_test, y_test, dt_entropy, così come accuracy_gini (che corrisponde all'accuratezza sul set di test ottenuta da dt_gini) sono disponibili nel tuo workspace.

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Machine Learning con modelli ad alberi in Python

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Istruzioni dell'esercizio

Importa accuracy_score da sklearn.metrics.
Predici le etichette del set di test con dt_entropy e assegna il risultato a y_pred.
Valuta l'accuratezza sul set di test di dt_entropy e assegna il risultato a accuracy_entropy.
Confronta accuracy_entropy e accuracy_gini.

Esercizio pratico interattivo

Prova a risolvere questo esercizio completando il codice di esempio.

# Import accuracy_score from sklearn.metrics
from ____.____ import ____

# Use dt_entropy to predict test set labels
____= ____.____(____)

# Evaluate accuracy_entropy
accuracy_entropy = ____(____, ____)

# Print accuracy_entropy
print(f'Accuracy achieved by using entropy: {accuracy_entropy:.3f}')

# Print accuracy_gini
print(f'Accuracy achieved by using the gini index: {accuracy_gini:.3f}')

Modifica ed esegui il codice

Questo esercizio fa parte del corso

Machine Learning con modelli ad alberi in Python

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Classification and Regression Trees (CART) are a set of supervised learning models used for problems involving classification and regression. In this chapter, you'll be introduced to the CART algorithm.

Exercise 1: Albero decisionale per la classificazione Exercise 2: Allena il tuo primo albero di classificazione Exercise 3: Valuta l'albero di classificazione Exercise 4: Regressione logistica vs albero di classificazione Exercise 5: Apprendimento dell'albero di classificazione Exercise 6: Far crescere un albero di classificazione Exercise 7: Usare l'entropia come criterio Exercise 8: Entropia vs indice di Gini

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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