Eseguire un modello usando intervalli

Hai appena finito di creare un elenco di hyperparameter e relativi intervalli da usare per il tuning di un modello predittivo per un compito. Hai usato max_depth, min_samples_split e max_features come nomi delle variabili degli intervalli.

Questo esercizio fa parte del corso

Validazione dei modelli in Python

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Istruzioni dell'esercizio

Seleziona in modo casuale max_depth, min_samples_split e max_features usando le tue variabili di intervallo.
Stampa tutti i parametri di rfr per vedere quali valori sono stati selezionati casualmente.

Esercizio pratico interattivo

Prova a risolvere questo esercizio completando il codice di esempio.

from sklearn.ensemble import RandomForestRegressor

# Fill in rfr using your variables
rfr = RandomForestRegressor(
    n_estimators=100,
    max_depth=random.____(____),
    min_samples_split=random.____(____),
    max_features=random.____(____))

# Print out the parameters
print(rfr.____)

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: 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: Introduzione all'hyperparameter tuning Exercise 2: Creare gli iperparametri Exercise 3: Eseguire un modello usando intervalli

Esercizio in corso

Exercise 4: RandomizedSearchCV Exercise 5: Preparazione a RandomizedSearch Exercise 6: Implementare RandomizedSearchCV Exercise 7: Selezionare il modello finale Exercise 8: Migliore accuratezza di classificazione Exercise 9: Selezionare il modello con la precision migliore Exercise 10: Corso completato!