En iyi sonuçları analiz etme

Günün sonunda, bir grid search içindeki en iyi performans gösteren 'kare' ile ilgileniyoruz. Neyse ki Scikit Learn'ün gridSearchCv nesnelerinde yalnızca en iyi kareye (veya cv_results_ içindeki satıra) dair önemli bilgileri veren birkaç parametre var.

İnceleyeceğin üç özellik:

best_score_ – En iyi performans gösteren karenin skoru (burada ROC_AUC).
best_index_ – En iyi performans gösteren kareye ait bilgileri içeren cv_results_ içindeki satırın indeksi.
best_params_ – En iyi skoru veren parametrelerin sözlüğü; örneğin 'max_depth': 10

grid_rf_class adlı grid search nesnesi hazır durumda.

Senin için satır 6'da cv_results_'dan oluşturulmuş bir dataframe (cv_results_df) var. Bu, sonuçlarda indeksleme yapmana yardımcı olacak.

Bu egzersiz

Python'da Hiperparametre Ayarlama

kursunun bir parçasıdır

Kursu Görüntüle

Egzersiz talimatları

grid_rf_class içindeki en iyi performans gösteren kareden ROC_AUC skorunu çıkar ve yazdır.
cv_results_df içinde indeksleme yaparak en iyi performans gösteren satırdan bir değişken oluştur.
grid_rf_class içindeki en iyi performans gösteren kareden n_estimators parametresini çıkararak best_n_estimators adlı bir değişken oluştur ve yazdır.

Uygulamalı interaktif egzersiz

Bu örnek kodu tamamlayarak bu egzersizi bitirin.

# Print out the ROC_AUC score from the best-performing square
best_score = grid_rf_class._____
print(best_score)

# Create a variable from the row related to the best-performing square
cv_results_df = pd.DataFrame(grid_rf_class.cv_results_)
best_row = cv_results_df.loc[[grid_rf_class.____]]
print(best_row)

# Get the n_estimators parameter from the best-performing square and print
best_n_estimators = grid_rf_class.____["_____"]
print(best_n_estimators)

Kodu Düzenle ve Çalıştır

Bu egzersiz

Python'da Hiperparametre Ayarlama

kursunun bir parçasıdır

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Kursa Ücretsiz Başlayın

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: Grid Search’e Giriş Exercise 2: Grid Search fonksiyonları oluştur Exercise 3: Birden çok hiperparametreyi yinelemeli olarak ayarla Exercise 4: Kaç Model?Exercise 5: Scikit Learn ile Grid Search Exercise 6: GridSearchCV girdileri Exercise 7: Scikit Learn ile GridSearchCV Exercise 8: Grid search çıktısını anlama Exercise 9: En iyi çıktıları kullanma Exercise 10: Grid search sonuçlarını keşfetme Exercise 11: En iyi sonuçları analiz etme

Geçerli Egzersiz

Exercise 12: En iyi sonuçları kullanma

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!