Memvisualisasikan Coarse to Fine

Anda akan menjalankan bagian pertama dari pencarian Coarse to Fine. Ini melibatkan menganalisis hasil pencarian acak awal yang dilakukan pada ruang pencarian yang luas, lalu memutuskan langkah logis berikutnya untuk membuat pencarian hyperparameter Anda menjadi lebih rinci.

Anda memiliki:

combinations_list - daftar kombinasi hyperparameter yang mungkin, tempat pencarian acak dilakukan.
results_df - DataFrame yang memuat setiap kombinasi hyperparameter dan akurasi yang dihasilkan dari seluruh 500 percobaan. Setiap hyperparameter adalah sebuah kolom, dengan header berupa nama hyperparameter.
visualize_hyperparameter() - fungsi yang menerima sebuah kolom DataFrame (sebagai string) dan menghasilkan plot sebar (scatter plot) dari nilai kolom tersebut dibandingkan skor akurasi. Contoh pemanggilan fungsi adalah visualize_hyperparameter('accuracy')

Jika Anda ingin melihat definisi fungsi visualize_hyperparameter(), Anda dapat menjalankan kode berikut:

import inspect
print(inspect.getsource(visualize_hyperparameter))

Latihan ini adalah bagian dari kursus

Penyetelan Hyperparameter di Python

Petunjuk latihan

Konfirmasikan (dengan mencetak) ukuran combinations_list, untuk membenarkan perlunya memulai dengan pencarian acak.
Urutkan results_df berdasarkan nilai akurasi dan cetak 10 baris teratas. Apakah ada wawasan yang jelas? Waspadai ukuran sampel yang kecil!
Konfirmasikan (dengan mencetak) hyperparameter mana yang digunakan dalam pencarian ini. Ini adalah nama kolom di results_df.
Panggil visualize_hyperparameter() untuk masing-masing hyperparameter secara bergiliran (max_depth, min_samples_leaf, learn_rate). Adakah tren yang terlihat?

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Confirm the size of the combinations_list
print(____(____))

# Sort the results_df by accuracy and print the top 10 rows
print(results_df.____(by=____, ascending=False).head(____))

# Confirm which hyperparameters were used in this search
print(results_df.____)

# Call visualize_hyperparameter() with each hyperparameter in turn
visualize_hyperparameter(____)
visualize_hyperparameter(____)
visualize_hyperparameter(____)

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: 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: Dari Kasar ke Rinci Exercise 2: Memvisualisasikan Coarse to Fine

Latihan Saat Ini

Exercise 3: Iterasi Coarse to Fine Exercise 4: Informed Search: Statistika Bayesian Exercise 5: Aturan Bayes di Python Exercise 6: Penyetelan Hyperparameter Bayesian dengan Hyperopt Exercise 7: Pencarian Terinformasi: Algoritme Genetika Exercise 8: Penyetelan Hyperparameter Genetik dengan TPOT Exercise 9: Menganalisis kestabilan TPOT Exercise 10: Selamat!