Leave-one-out cross-validation

Supponiamo che la tua caramella preferita non sia presente nell'insieme di dati delle caramelle e che tu voglia stimarne la popolarità. Usare una cross-validation con 5 fold allena il modello solo sull’80% dei dati alla volta. Tuttavia, il dataset delle caramelle ha solo 85 righe e lasciare fuori il 20% dei dati potrebbe penalizzare il nostro modello. Con la leave-one-out cross-validation, invece, possiamo sfruttare al massimo il nostro insieme di dati limitato e ottenere la stima migliore della popolarità della tua caramella preferita!

In questo esercizio userai cross_val_score() per eseguire la LOOCV.

Questo esercizio fa parte del corso

Validazione dei modelli in Python

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Istruzioni dell'esercizio

Crea uno scorer usando mean_absolute_error da passare a cross_val_score().
Completa cross_val_score() in modo che utilizzi il modello rfr, il nuovo mae_scorer e la LOOCV.
Stampa la media e la deviazione standard di scores usando numpy (importato come np).

Esercizio pratico interattivo

Prova a risolvere questo esercizio completando il codice di esempio.

from sklearn.metrics import mean_absolute_error, make_scorer

# Create scorer
mae_scorer = ____(____)

rfr = RandomForestRegressor(n_estimators=15, random_state=1111)

# Implement LOOCV
scores = cross_val_score(____, X=X, y=y, cv=____, scoring=____)

# Print the mean and standard deviation
print("The mean of the errors is: %s." % np.____(____))
print("The standard deviation of the errors is: %s." % np.____(____))

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: Creating train, test, and validation datasets Exercise 2: Create one holdout set Exercise 3: Create two holdout sets Exercise 4: Why use holdout sets Exercise 5: Accuracy metrics: regression models Exercise 6: Mean absolute error Exercise 7: Mean squared error Exercise 8: Performance on data subsets Exercise 9: Classification metrics Exercise 10: Confusion matrices Exercise 11: Confusion matrices, again Exercise 12: Precision vs. recall Exercise 13: The bias-variance tradeoff Exercise 14: Error due to under/over-fitting Exercise 15: Am I underfitting?

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: I limiti dei set di holdout Exercise 2: Due campioni Exercise 3: Problemi potenziali Exercise 4: Validazione incrociata Exercise 5: `KFold()` di scikit-learn Exercise 6: Utilizzare gli indici di KFold Exercise 7: cross_val_score() di sklearn Exercise 8: Metodi di scikit-learn Exercise 9: Usa cross_val_score()Exercise 10: Leave-one-out cross-validation (LOOCV)Exercise 11: Quando usare LOOCV Exercise 12: Leave-one-out cross-validation

Esercizio in corso

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!