Valuta l’albero ottimale

In questo esercizio valuterai l’AUC ROC sul test set del modello ottimale di grid_dt.

Per farlo, prima determinerai la probabilità di ottenere l’etichetta positiva per ciascuna osservazione del test set. Puoi usare il metodo predict_proba() di un classificatore di scikit-learn per calcolare un array 2D che contiene, rispettivamente per colonne, le probabilità delle etichette di classe negativa e positiva.

Il dataset è già caricato e preprocessato per te (le caratteristiche numeriche sono standardizzate); è suddiviso in 80% train e 20% test. X_test, y_test sono disponibili nel tuo workspace. Inoltre, abbiamo già caricato l’oggetto GridSearchCV addestrato grid_dt che hai istanziato nell’esercizio precedente. Nota che grid_dt è stato addestrato come segue:

grid_dt.fit(X_train, y_train)

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Machine Learning con modelli ad alberi in Python

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Istruzioni dell'esercizio

Importa roc_auc_score da sklearn.metrics.
Estrai l’attributo .best_estimator_ da grid_dt e assegnalo a best_model.
Predici le probabilità del test set di ottenere la classe positiva in y_pred_proba.
Calcola l’AUC ROC sul test set test_roc_auc di best_model.

Esercizio pratico interattivo

Prova a risolvere questo esercizio completando il codice di esempio.

# Import roc_auc_score from sklearn.metrics
____

# Extract the best estimator
best_model = ____

# Predict the test set probabilities of the positive class
y_pred_proba = ____

# Compute test_roc_auc
test_roc_auc = ____

# Print test_roc_auc
print('Test set ROC AUC score: {:.3f}'.format(test_roc_auc))

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: Decision tree for classification Exercise 2: Train your first classification tree Exercise 3: Evaluate the classification tree Exercise 4: Logistic regression vs classification tree Exercise 5: Classification tree Learning Exercise 6: Growing a classification tree Exercise 7: Using entropy as a criterion Exercise 8: Entropy vs Gini index Exercise 9: Decision tree for regression Exercise 10: Train your first regression tree Exercise 11: Evaluate the regression tree Exercise 12: Linear regression vs regression tree

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Exercise 1: Generalization Error Exercise 2: Complexity, bias and variance Exercise 3: Overfitting and underfitting Exercise 4: Diagnose bias and variance problems Exercise 5: Instantiate the model Exercise 6: Evaluate the 10-fold CV error Exercise 7: Evaluate the training error Exercise 8: High bias or high variance?Exercise 9: Ensemble Learning Exercise 10: Define the ensemble Exercise 11: Evaluate individual classifiers Exercise 12: Better performance with a Voting Classifier

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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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Exercise 1: Ottimizzare gli iperparametri di un CART Exercise 2: Iperparametri dell'albero Exercise 3: Imposta la griglia di iperparametri dell'albero Exercise 4: Cerca l'albero ottimale Exercise 5: Valuta l’albero ottimale

Esercizio in corso

Exercise 6: Ottimizzare gli iperparametri di una RF Exercise 7: Iperparametri delle Random Forest Exercise 8: Imposta la griglia di iperparametri di RF Exercise 9: Cerca la foresta ottimale Exercise 10: Valuta la foresta ottimale Exercise 11: Congratulazioni!