La regola di Bayes in Python

In questo esercizio metterai in pratica un esempio concreto: imposterai la formula di Bayes, acquisirai nuove evidenze e aggiornerai le tue “credenze” per ottenere un risultato più accurato. L’esempio riguarda la probabilità che qualcuno chiuda il proprio account per il tuo prodotto software online.

Queste sono le probabilità note:

Il 7% (0,07) delle persone probabilmente chiuderà l’account il mese prossimo
Il 15% (0,15) delle persone con un account è insoddisfatto del tuo prodotto (ma non sai chi!)
Il 35% (0,35) delle persone che probabilmente chiuderanno l’account è insoddisfatto del tuo prodotto

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Ottimizzazione degli iperparametri in Python

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# Assign probabilities to variables 
p_unhappy = ____
p_unhappy_close = ____

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Ottimizzazione degli iperparametri in Python

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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: Ricerca informata: dal grossolano al fine Exercise 2: Visualizzare Coarse to Fine Exercise 3: Iterazioni Coarse to Fine Exercise 4: Ricerca informata: statistica bayesiana Exercise 5: La regola di Bayes in Python

Esercizio attuale

Exercise 6: Tuning bayesiano degli iperparametri con Hyperopt Exercise 7: Informed Search: Algoritmi genetici Exercise 8: Ottimizzazione genetica degli iperparametri con TPOT Exercise 9: Analizzare la stabilità di TPOT Exercise 10: Congratulazioni!