Paramètres dans la régression logistique

Maintenant que vous avez exploré ce qu’est un paramètre, mettons cela en pratique. Il est important, face à tout nouvel algorithme, d’identifier quels éléments sont des paramètres et lesquels sont des hyperparamètres.

Lequel des éléments suivants est un paramètre du modèle de régression logistique Scikit Learn ? Ici, nous parlons au sens conceptuel, d’après la théorie introduite dans ce cours, et PAS de ce que la documentation Scikit Learn appelle un paramètre ou un attribut.

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<cours>Optimisation des hyperparamètres en Python</cours>

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<cours>Optimisation des hyperparamètres en Python</cours>

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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 et « Paramètres »Exercise 2: Paramètres dans la régression logistique

Exercice actuel

Exercise 3: Extraire un paramètre de régression logistique Exercise 4: Extraire un paramètre d’un Random Forest Exercise 5: Introduction aux hyperparamètres Exercise 6: Hyperparamètres dans les Random Forests Exercise 7: Explorer les hyperparamètres de Random Forest Exercise 8: Hyperparamètres de KNN Exercise 9: Définir et analyser les valeurs d’hyperparamètres Exercise 10: Automatiser le choix des hyperparamètres Exercise 11: Construire des courbes d’apprentissage

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: 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!