Menjelajahi hasil grid search

Sekarang Anda akan menelusuri properti cv_results_ dari objek GridSearchCV yang didefinisikan dalam video. Ini adalah sebuah dictionary yang dapat kita baca ke dalam pandas DataFrame dan berisi banyak informasi berguna tentang grid search yang baru saja kita lakukan.

Pengingat mengenai berbagai tipe kolom dalam properti ini:

kolom time_
kolom param_ (satu untuk setiap hyperparameter) dan kolom params tunggal (dengan semua pengaturan hyperparameter)
satu kolom train_score untuk setiap lipatan cv termasuk kolom mean_train_score dan std_train_score
satu kolom test_score untuk setiap lipatan cv termasuk kolom mean_test_score dan std_test_score
satu kolom rank_test_score dengan angka dari 1 hingga n (jumlah iterasi) yang memberi peringkat baris berdasarkan mean_test_score

Latihan ini adalah bagian dari kursus

Penyetelan Hyperparameter di Python

Petunjuk latihan

Baca properti cv_results_ dari objek GridSearchCV grid_rf_class ke dalam sebuah data frame & cetak seluruhnya untuk diperiksa.
Ekstrak & cetak kolom tunggal yang berisi dictionary semua hyperparameter yang digunakan pada setiap iterasi grid search.
Ekstrak & cetak baris yang memiliki mean test score terbaik dengan melakukan pengindeksan menggunakan kolom rank_test_score.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# 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)

Edit dan Jalankan Kode

Latihan ini adalah bagian dari kursus

Penyetelan Hyperparameter di Python

SkillTag.level.intermediateSkillTag.label

4.9+

Mulai Kursus Gratis

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: Pengenalan Grid Search Exercise 2: Bangun fungsi Grid Search Exercise 3: Melakukan tuning beberapa hyperparameter secara iteratif Exercise 4: Berapa Banyak Model?Exercise 5: Grid Search dengan Scikit Learn Exercise 6: Masukan GridSearchCV Exercise 7: GridSearchCV dengan Scikit Learn Exercise 8: Memahami keluaran grid search Exercise 9: Menggunakan keluaran terbaik Exercise 10: Menjelajahi hasil grid search

Latihan Saat Ini

Exercise 11: Menganalisis hasil terbaik Exercise 12: Menggunakan hasil terbaik

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!