Input di GridSearchCV

Mettiamo alla prova la tua conoscenza degli input di GridSeachCV rispondendo alla domanda qui sotto.

Nella console sono disponibili tre oggetti GridSearchCV, chiamati model_1, model_2, model_3. Nota che non ci sono dati con cui fare il fit di questi modelli. Devi quindi rispondere osservando solo come sono stati costruiti.

Quale di questi oggetti GridSearchCV non funzionerebbe quando proviamo a fare il fit?

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Ottimizzazione degli iperparametri in Python

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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: Introduzione a Grid Search Exercise 2: Crea funzioni per la Grid Search Exercise 3: Ottimizza iterativamente più iperparametri Exercise 4: Quanti modelli?Exercise 5: Grid Search con Scikit Learn Exercise 6: Input di GridSearchCV

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Exercise 7: GridSearchCV con Scikit Learn Exercise 8: Capire l'output di una grid search Exercise 9: Usare i migliori risultati Exercise 10: Esplorare i risultati della grid search Exercise 11: Analizzare i risultati migliori Exercise 12: Usare i risultati migliori

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!