Perbandingan Grid dan Random Search secara Berdampingan

Memvisualisasikan ruang pencarian untuk random search dan grid search secara bersamaan memudahkan Anda melihat cakupan masing-masing teknik, serta menghidupkan keunggulan dan keterbatasan spesifiknya.

Dalam latihan ini, Anda akan mengambil sampel kombinasi hiperparameter dengan cara grid search dan random search, lalu memplotnya untuk melihat perbedaannya.

Anda akan memiliki:

combinations_list yaitu daftar kombinasi learn_rate dan min_samples_leaf untuk algoritma ini
Fungsi visualize_search() yang akan mengubah kombinasi hiperparameter Anda menjadi koordinat X dan Y serta memplot kombinasi grid dan random search pada grafik yang sama. Fungsi ini menerima dua daftar kombinasi hiperparameter sebagai masukan.

Jika Anda ingin melihat definisi fungsi visualize_search(), jalankan kode berikut:

import inspect
print(inspect.getsource(visualize_search))

Latihan ini adalah bagian dari kursus

Penyetelan Hyperparameter di Python

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Sample grid coordinates
grid_combinations_chosen = ____[0:____]

# Print result
print(____)

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 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

Latihan Saat Ini

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!