Valuta i singoli classificatori

In questo esercizio valuterai le prestazioni dei modelli nella lista classifiers che abbiamo definito nell'esercizio precedente. Lo farai addestrando ciascun classificatore sul training set e valutandone l'accuratezza sul test set.

Il dataset è già caricato e preprocessato per te (le caratteristiche numeriche sono standardizzate) ed è suddiviso in 70% train e 30% test. Le matrici delle caratteristiche X_train e X_test, così come gli array di etichette y_train e y_test, sono disponibili nel tuo workspace. Inoltre, abbiamo caricato la lista classifiers dall'esercizio precedente, insieme alla funzione accuracy_score() da sklearn.metrics.

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Machine Learning con modelli ad alberi in Python

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Istruzioni dell'esercizio

Itera sulle tuple in classifiers. Usa clf_name e clf come variabili del ciclo for:
- Addestra clf sul training set.
- Predici le etichette del test set con clf e assegna i risultati a y_pred.
- Valuta l'accuratezza di clf sul test set e stampa il risultato.

Esercizio pratico interattivo

Prova a risolvere questo esercizio completando il codice di esempio.

# Iterate over the pre-defined list of classifiers
for clf_name, clf in ____:    
 
    # Fit clf to the training set
    ____.____(____, ____)    
   
    # Predict y_pred
    y_pred = ____.____(____)
    
    # Calculate accuracy
    accuracy = ____(____, ____) 
   
    # Evaluate clf's accuracy on the test set
    print('{:s} : {:.3f}'.format(clf_name, accuracy))

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Questo esercizio fa parte del corso

Machine Learning con modelli ad alberi in Python

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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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