Grid ve Random Search Yan Yana

Random ve grid search'ün arama uzayını birlikte görselleştirmek, her tekniğin kapsama alanını kolayca görmene ve böylece kendilerine özgü avantaj ve dezavantajlarını canlandırmana yardımcı olur.

Bu egzersizde, hiperparametre kombinasyonlarını hem grid search hem de random search şeklinde örnekleyecek, ardından farkı görmek için bunları görselleştireceksin.

Elinde şunlar olacak:

Bu algoritma için learn_rate ve min_samples_leaf kombinasyonlarının listesi olan combinations_list
Hiperparametre kombinasyonlarını X ve Y koordinatlarına çevirip aynı grafikte hem grid hem de random search kombinasyonlarını çizecek visualize_search() fonksiyonu. Girdi olarak iki adet hiperparametre kombinasyon listesi alır.

visualize_search() fonksiyonunun tanımını görmek istersen, şu kodu çalıştırabilirsin:

import inspect
print(inspect.getsource(visualize_search))

Bu egzersiz

Python'da Hiperparametre Ayarlama

kursunun bir parçasıdır

Kursu Görüntüle

Uygulamalı interaktif egzersiz

Bu örnek kodu tamamlayarak bu egzersizi bitirin.

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

# Print result
print(____)

Kodu Düzenle ve Çalıştır

Bu egzersiz

Python'da Hiperparametre Ayarlama

kursunun bir parçasıdır

IntermediárioNível de habilidade

4.9+

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: 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: Random Search’e Giriş Exercise 2: Hiperparametreleri Rastgele Örnekle Exercise 3: Random Forest ile Rastgele Arama Exercise 4: Random Search’ü Görselleştirme Exercise 5: Scikit Learn'de Random Search Exercise 6: RandomSearchCV girdileri Exercise 7: RandomizedSearchCV Nesnesi Exercise 8: Scikit Learn'de RandomSearchCV Exercise 9: Grid Search ve Random Search Karşılaştırması Exercise 10: Rastgele Arama ve Izgara Aramasını Karşılaştırma Exercise 11: Grid ve Random Search Yan Yana

Geçerli Egzersiz

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!