Règle de Bayes en Python

Dans cet exercice, vous allez travailler un exemple concret : formuler la règle de Bayes, intégrer une nouvelle information et mettre à jour vos « croyances » pour obtenir un résultat plus précis. L’exemple porte sur la probabilité qu’une personne ferme son compte pour votre logiciel en ligne.

Voici les probabilités connues :

7 % (0,07) des personnes sont susceptibles de fermer leur compte le mois prochain
15 % (0,15) des titulaires de compte sont mécontents de votre produit (vous ne savez pas lesquels)
35 % (0,35) des personnes susceptibles de fermer leur compte sont mécontentes de votre produit

Cet exercice fait partie du cours

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

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Exercice interactif pratique

Essayez cet exercice en complétant ce code d’exemple.

# Assign probabilities to variables 
p_unhappy = ____
p_unhappy_close = ____

Modifier et exécuter le code

Cet exercice fait partie du cours

<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 & '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: Recherche informée : du global au fin Exercise 2: Visualiser la méthode « du grossier au précis »Exercise 3: Itérations du grossier au fin Exercise 4: Recherche informée : statistiques bayésiennes Exercise 5: Règle de Bayes en Python

Exercice actuel

Exercise 6: Optimisation bayésienne des hyperparamètres avec Hyperopt Exercise 7: Recherche informée : algorithmes génétiques Exercise 8: Réglage génétique des hyperparamètres avec TPOT Exercise 9: Analyser la stabilité de TPOT Exercise 10: Félicitations !