Preparazione a RandomizedSearch

Lo scorso semestre il tuo professore ha sfidato la classe a costruire un modello predittivo per prevedere i punteggi dell’esame finale. Hai provato a eseguire alcuni modelli selezionando gli iperparametri in modo casuale. Tuttavia, l’esecuzione di ciascun modello richiedeva di programmarlo individualmente.

Dopo aver imparato a usare RandomizedSearchCV(), stai riprendendo la sfida del professore per costruire il modello migliore. In questo esercizio preparerai i tre input necessari per completare una ricerca casuale.

Questo esercizio fa parte del corso

Validazione dei modelli in Python

Visualizza il corso

Istruzioni dell'esercizio

Completa il dizionario dei parametri aggiungendo una lista per il parametro max_depth con le opzioni 2, 4, 6 e 8.
Crea un modello di regressione random forest con dieci alberi e random_state impostato a 1111.
Crea uno scorer di mean squared error da utilizzare.

Esercizio pratico interattivo

Prova a risolvere questo esercizio completando il codice di esempio.

from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import make_scorer, mean_squared_error

# Finish the dictionary by adding the max_depth parameter
param_dist = {"____": [____],
              "max_features": [2, 4, 6, 8, 10],
              "min_samples_split": [2, 4, 8, 16]}

# Create a random forest regression model
rfr = ____(____=10, ____=1111)

# Create a scorer to use (use the mean squared error)
scorer = ____(____)

Modifica ed esegui il codice

Questo esercizio fa parte del corso

Validazione dei modelli in Python

IntermediárioNível de habilidade

4.9+

Inizia il corso gratis

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 Exercise 4: RandomizedSearchCV Exercise 5: Preparazione a RandomizedSearch

Esercizio in corso

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!