Esplorare i risultati della grid search

Ora esplorerai la proprietà cv_results_ dell'oggetto GridSearchCV definito nel video. È un dizionario che possiamo leggere in un DataFrame di pandas e contiene molte informazioni utili sulla grid search che abbiamo appena eseguito.

Un promemoria dei diversi tipi di colonne in questa proprietà:

colonne time_
colonne param_ (una per ogni iperparametro) e l'unica colonna params (con tutte le impostazioni degli iperparametri)
una colonna train_score per ogni fold della cv, incluse le colonne mean_train_score e std_train_score
una colonna test_score per ogni fold della cv, incluse le colonne mean_test_score e std_test_score
una colonna rank_test_score con un numero da 1 a n (numero di iterazioni) che ordina le righe in base al loro mean_test_score

Questo esercizio fa parte del corso

Ottimizzazione degli iperparametri in Python

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Istruzioni dell'esercizio

Leggi la proprietà cv_results_ dell'oggetto GridSearchCV grid_rf_class in un DataFrame e stampalo interamente per ispezionarlo.
Estrai e stampa la colonna singolare che contiene un dizionario di tutti gli iperparametri usati in ciascuna iterazione della grid search.
Estrai e stampa la riga che ha avuto il miglior mean test score indicizzando tramite la colonna rank_test_score.

Esercizio pratico interattivo

Prova a risolvere questo esercizio completando il codice di esempio.

# Read the cv_results property into a dataframe & print it out
cv_results_df = pd.DataFrame(grid_rf_class.____)
print(____)

# Extract and print the column with a dictionary of hyperparameters used
column = cv_results_df.loc[:, [____]]
print(____)

# Extract and print the row that had the best mean test score
best_row = cv_results_df[cv_results_df[____] == ____ ]
print(best_row)

Modifica ed esegui il codice

Questo esercizio fa parte del corso

Ottimizzazione degli iperparametri in Python

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In this introductory chapter you will learn the difference between hyperparameters and parameters. You will practice extracting and analyzing parameters, setting hyperparameter values for several popular machine learning algorithms. Along the way you will learn some best practice tips & tricks for choosing which hyperparameters to tune and what values to set & build learning curves to analyze your hyperparameter choices.

Exercise 1: Introduction & 'Parameters'Exercise 2: Parameters in Logistic Regression Exercise 3: Extracting a Logistic Regression parameter Exercise 4: Extracting a Random Forest parameter Exercise 5: Introducing Hyperparameters Exercise 6: Hyperparameters in Random Forests Exercise 7: Exploring Random Forest Hyperparameters Exercise 8: Hyperparameters of KNN Exercise 9: Setting & Analyzing Hyperparameter Values Exercise 10: Automating Hyperparameter Choice Exercise 11: Building Learning Curves

This chapter introduces you to a popular automated hyperparameter tuning methodology called Grid Search. You will learn what it is, how it works and practice undertaking a Grid Search using Scikit Learn. You will then learn how to analyze the output of a Grid Search & gain practical experience doing this.

Exercise 1: Introduzione a Grid Search Exercise 2: Crea funzioni per la Grid Search Exercise 3: Ottimizza iterativamente più iperparametri Exercise 4: Quanti modelli?Exercise 5: Grid Search con Scikit Learn Exercise 6: Input di GridSearchCV Exercise 7: GridSearchCV con Scikit Learn Exercise 8: Capire l'output di una grid search Exercise 9: Usare i migliori risultati Exercise 10: Esplorare i risultati della grid search

Esercizio in corso

Exercise 11: Analizzare i risultati migliori Exercise 12: Usare i risultati migliori

In this chapter you will be introduced to another popular automated hyperparameter tuning methodology called Random Search. You will learn what it is, how it works and importantly how it differs from grid search. You will learn some advantages and disadvantages of this method and when to choose this method compared to Grid Search. You will practice undertaking a Random Search with Scikit Learn as well as visualizing & interpreting the output.

Exercise 1: Introducing Random Search Exercise 2: Randomly Sample Hyperparameters Exercise 3: Randomly Search with Random Forest Exercise 4: Visualizing a Random Search Exercise 5: Random Search in Scikit Learn Exercise 6: RandomSearchCV inputs Exercise 7: The RandomizedSearchCV Object Exercise 8: RandomSearchCV in Scikit Learn Exercise 9: Comparing Grid and Random Search Exercise 10: Comparing Random & Grid Search Exercise 11: Grid and Random Search Side by Side

In this final chapter you will be given a taste of more advanced hyperparameter tuning methodologies known as ''informed search''. This includes a methodology known as Coarse To Fine as well as Bayesian & Genetic hyperparameter tuning algorithms. You will learn how informed search differs from uninformed search and gain practical skills with each of the mentioned methodologies, comparing and contrasting them as you go.

Exercise 1: Informed Search: Coarse to Fine Exercise 2: Visualizing Coarse to Fine Exercise 3: Coarse to Fine Iterations Exercise 4: Informed Search: Bayesian Statistics Exercise 5: Bayes Rule in Python Exercise 6: Bayesian Hyperparameter tuning with Hyperopt Exercise 7: Informed Search: Genetic Algorithms Exercise 8: Genetic Hyperparameter Tuning with TPOT Exercise 9: Analysing TPOT's stability Exercise 10: Congratulations!