Precision vs. recall

Le metriche di accuratezza che usi per valutare il tuo modello dovrebbero sempre dipendere dall’applicazione specifica. In questo esempio, immaginiamo che tu non sopporti perdere a Tris, ma solo quando sei certo di stare per vincere.

Scegli la metrica di accuratezza più adatta, tra precision e recall, per completare questo esempio. Ma ricorda: se pensi che stai per vincere, allora devi vincere!

Usa rfc, un modello di classificazione random forest costruito sul dataset tic_tac_toe.

Questo esercizio fa parte del corso

Validazione dei modelli in Python

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Istruzioni dell'esercizio

Importa da sklearn la metrica precision o recall. Solo un metodo è corretto nel contesto dato.
Calcola precision o recall usando y_test per i valori veri e test_predictions per le previsioni.
Stampa il punteggio finale in base alla metrica scelta.

Esercizio pratico interattivo

Prova a risolvere questo esercizio completando il codice di esempio.

from sklearn.metrics import ____

test_predictions = rfc.predict(X_test)

# Create precision or recall score based on the metric you imported
score = ____(____, ____)

# Print the final result
print("The ____ value is {0:.2f}".format(____))

Modifica ed esegui il codice

Questo esercizio fa parte del corso

Validazione dei modelli in Python

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Before we can validate models, we need an understanding of how to create and work with them. This chapter provides an introduction to running regression and classification models in scikit-learn. We will use this model building foundation throughout the remaining chapters.

Exercise 1: Introduction to model validation Exercise 2: Modeling steps Exercise 3: Seen vs. unseen data Exercise 4: Regression models Exercise 5: Set parameters and fit a model Exercise 6: Feature importances Exercise 7: Classification models Exercise 8: Classification predictions Exercise 9: Reusing model parameters Exercise 10: Random forest classifier

This chapter focuses on the basics of model validation. From splitting data into training, validation, and testing datasets, to creating an understanding of the bias-variance tradeoff, we build the foundation for the techniques of K-Fold and Leave-One-Out validation practiced in chapter three.

Exercise 1: Creare insiemi di dati di training, test e validation Exercise 2: Crea un set di holdout Exercise 3: Crea due holdout set Exercise 4: Perché usare gli insiemi di holdout Exercise 5: Metriche di accuratezza: modelli di regressione Exercise 6: Errore assoluto medio Exercise 7: Errore quadratico medio Exercise 8: Prestazioni su sottoinsiemi di dati Exercise 9: Metriche di classificazione Exercise 10: Matrici di confusione Exercise 11: Ancora sulle confusion matrix Exercise 12: Precision vs. recall

Esercizio in corso

Exercise 13: Il compromesso bias-varianza Exercise 14: Errore dovuto a under/overfitting Exercise 15: Sto underfittando?

Holdout sets are a great start to model validation. However, using a single train and test set if often not enough. Cross-validation is considered the gold standard when it comes to validating model performance and is almost always used when tuning model hyper-parameters. This chapter focuses on performing cross-validation to validate model performance.

Exercise 1: The problems with holdout sets Exercise 2: Two samples Exercise 3: Potential problems Exercise 4: Cross-validation Exercise 5: scikit-learn's KFold()Exercise 6: Using KFold indices Exercise 7: sklearn's cross_val_score()Exercise 8: scikit-learn's methods Exercise 9: Implement cross_val_score()Exercise 10: Leave-one-out-cross-validation (LOOCV)Exercise 11: When to use LOOCV Exercise 12: Leave-one-out-cross-validation

The first three chapters focused on model validation techniques. In chapter 4 we apply these techniques, specifically cross-validation, while learning about hyperparameter tuning. After all, model validation makes tuning possible and helps us select the overall best model.

Exercise 1: Introduction to hyperparameter tuning Exercise 2: Creating Hyperparameters Exercise 3: Running a model using ranges Exercise 4: RandomizedSearchCV Exercise 5: Preparing for RandomizedSearch Exercise 6: Implementing RandomizedSearchCV Exercise 7: Selecting your final model Exercise 8: Best classification accuracy Exercise 9: Selecting the best precision model Exercise 10: Course completed!