Allena il classificatore AdaBoost

Ora che hai istanziato il classificatore AdaBoost ada, è il momento di addestrarlo. Prevedrai anche le probabilità di ottenere la classe positiva nel set di test. Puoi farlo così:

Una volta addestrato il classificatore ada, richiama il metodo .predict_proba() passando X_test come parametro ed estrai queste probabilità selezionando tutti i valori della seconda colonna come segue:

ada.predict_proba(X_test)[:,1]

Il dataset Indian Liver è già stato elaborato e suddiviso in 80% train e 20% 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 ambiente di lavoro. Inoltre, abbiamo anche caricato il modello ada istanziato dall'esercizio precedente.

Questo esercizio fa parte del corso

Machine Learning con modelli ad alberi in Python

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Istruzioni dell'esercizio

Esegui il fit di ada sul training set.
Valuta le probabilità di ottenere la classe positiva nel set di test.

Esercizio pratico interattivo

Prova a risolvere questo esercizio completando il codice di esempio.

# Fit ada to the training set
____

# Compute the probabilities of obtaining the positive class
y_pred_proba = ____.____(____)[____]

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: 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: Definisci il classificatore AdaBoost Exercise 3: Allena il classificatore AdaBoost

Esercizio in corso

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!