Memvisualisasikan Random Search

Memvisualisasikan ruang pencarian dari random search memungkinkan Anda dengan mudah melihat cakupan teknik ini dan, karenanya, melihat dampak sampling Anda terhadap ruang pencarian.

Dalam latihan ini Anda akan menggunakan beberapa sampel berbeda dari kombinasi hyperparameter dan menghasilkan visualisasi ruang pencarian.

Fungsi sample_and_visualize_hyperparameters() menerima satu argumen (jumlah kombinasi yang akan diambil sampelnya) lalu secara acak melakukan sampling kombinasi hyperparameter, seperti yang Anda lakukan pada latihan sebelumnya! Fungsi tersebut kemudian akan memvisualisasikan kombinasi-kombinasi tersebut.

Jika Anda ingin melihat definisi fungsinya, Anda dapat menggunakan pustaka inspect di Python yang praktis, seperti berikut:

print(inspect.getsource(sample_and_visualize_hyperparameters))

Latihan ini adalah bagian dari kursus

Penyetelan Hyperparameter di Python

Petunjuk latihan

Konfirmasikan berapa banyak kemungkinan kombinasi hyperparameter dalam combinations_list dengan menetapkannya ke variabel number_combs dan cetak nilainya.
Lakukan sampling dan visualisasikan 50, 500, dan 1500 kombinasi. Anda akan menggunakan sebuah loop agar ringkas. Apa yang Anda amati dari visualisasinya?
Sekarang lakukan sampling dan visualisasikan seluruh kumpulan kombinasi. Anda sudah membuat variabel untuk membantu hal ini. Seperti apa hasilnya?

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Confirm how many hyperparameter combinations & print
number_combs = ____(____)
print(____)

# Sample and visualise specified combinations
for x in [____, ____, ____]:
    sample_and_visualize_hyperparameters(x)
    
# Sample all the hyperparameter combinations & visualise
____(____)

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: Introducing Grid Search Exercise 2: Build Grid Search functions Exercise 3: Iteratively tune multiple hyperparameters Exercise 4: How Many Models?Exercise 5: Grid Search with Scikit Learn Exercise 6: GridSearchCV inputs Exercise 7: GridSearchCV with Scikit Learn Exercise 8: Understanding a grid search output Exercise 9: Using the best outputs Exercise 10: Exploring the grid search results Exercise 11: Analyzing the best results Exercise 12: Using the best results

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: Pengenalan Random Search Exercise 2: Sampelkan Hyperparameter Secara Acak Exercise 3: Pencarian Acak dengan Random Forest Exercise 4: Memvisualisasikan Random Search

Latihan Saat Ini

Exercise 5: Random Search di Scikit Learn Exercise 6: Masukan RandomSearchCV Exercise 7: Objek RandomizedSearchCV Exercise 8: RandomSearchCV di Scikit Learn Exercise 9: Membandingkan Grid Search dan Random Search Exercise 10: Membandingkan Random & Grid Search Exercise 11: Perbandingan Grid dan Random Search secara Berdampingan

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!