Importanza delle feature

Anche se alcuni attributi delle caramelle, come il cioccolato, possono essere molto popolari, non significa che saranno importanti per la predizione del modello. Dopo aver addestrato un modello di random forest, puoi consultare l’attributo del modello .feature_importances_ per vedere quali variabili hanno avuto l’impatto maggiore. Puoi verificare quanto è stata importante ciascuna variabile nel modello iterando sull’array delle feature importances usando enumerate().

Se non conosci la funzione enumerate() di Python, ti permette di scorrere una lista creando anche un contatore automatico.

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Validazione dei modelli in Python

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Istruzioni dell'esercizio

Itera sull’output delle importanze delle feature di rfr.
Stampa i nomi delle colonne di X_train e il punteggio di importanza per ciascuna colonna.

Esercizio pratico interattivo

Prova a risolvere questo esercizio completando il codice di esempio.

# Fit the model using X and y
rfr.fit(X_train, y_train)

# Print how important each column is to the model
for i, item in enumerate(rfr.____):
      # Use i and item to print out the feature importance of each column
    print("{0:s}: {1:.2f}".format(X_train.columns[____], ____))

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: Introduzione alla validazione dei modelli Exercise 2: Fasi del modeling Exercise 3: Dati visti vs. dati mai visti Exercise 4: Modelli di regressione Exercise 5: Imposta i parametri e addestra un modello Exercise 6: Importanza delle feature

Esercizio in corso

Exercise 7: Modelli di classificazione Exercise 8: Predizioni di classificazione Exercise 9: Riutilizzare i parametri del modello Exercise 10: Classificatore random forest

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: 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!