Parâmetros em Regressão Logística

Agora que você teve a chance de explorar o que é um parâmetro, vamos aplicar esse conhecimento. É importante conseguir revisar qualquer novo algoritmo e identificar quais elementos são parâmetros e quais são hiperparâmetros.

Qual das opções a seguir é um parâmetro do modelo de regressão logística do Scikit Learn? Aqui queremos dizer de forma conceitual, com base na teoria apresentada neste curso. NÃO o que a documentação do Scikit Learn chama de parâmetro ou atributo.

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Ajuste de Hiperparâmetros em Python

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Ajuste de Hiperparâmetros em 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: Introdução e "Parâmetros"Exercise 2: Parâmetros em Regressão Logística

Exercicio Atual

Exercise 3: Extraindo um parâmetro de Regressão Logística Exercise 4: Extraindo um parâmetro de Random Forest Exercise 5: Apresentando hiperparâmetros Exercise 6: Hiperparâmetros em Random Forests Exercise 7: Explorando os hiperparâmetros de Random Forest Exercise 8: Hiperparâmetros do KNN Exercise 9: Definindo e analisando valores de hiperparâmetros Exercise 10: Automatizando a Escolha de Hiperparâmetros Exercise 11: Construindo curvas de aprendizado

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!