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Hyperparameter Tuning in Python

Learn techniques for automated hyperparameter tuning in Python, including Grid, Random, and Informed Search.

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4 horas13 vídeos44 exercícios19.567 aprendizesTrophyDeclaração de Realização

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Descrição do Curso

As a data or machine learning scientist, building powerful machine learning models depends heavily on the set of hyperparameters used. But with increasingly complex models with lots of options, how do you efficiently find the best settings for your particular problem? The answer is hyperparameter tuning!

Hyperparameters vs. parameters

Gain practical experience using various methodologies for automated hyperparameter tuning in Python with Scikit-Learn.

Learn the difference between hyperparameters and parameters and best practices for setting and analyzing hyperparameter values. This foundation will prepare you to understand the significance of hyperparameters in machine learning models.

Grid search

Master several hyperparameter tuning techniques, starting with Grid Search. Using credit card default data, you will practice conducting Grid Search to exhaustively search for the best hyperparameter combinations and interpret the results.

You will be introduced to Random Search, and learn about its advantages over Grid Search, such as efficiency in large parameter spaces.​

Informed search

In the final part of the course, you will explore advanced optimization methods, such as Bayesian and Genetic algorithms.

These informed search techniques are demonstrated through practical examples, allowing you to compare and contrast them with uninformed search methods. By the end, you will have a comprehensive understanding of how to optimize hyperparameters effectively to improve model performance​.
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  1. 1

    Hyperparameters and Parameters

    Gratuito

    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.

    Reproduzir Capítulo Agora
    Introduction & 'Parameters'
    50 xp
    Parameters in Logistic Regression
    50 xp
    Extracting a Logistic Regression parameter
    100 xp
    Extracting a Random Forest parameter
    100 xp
    Introducing Hyperparameters
    50 xp
    Hyperparameters in Random Forests
    50 xp
    Exploring Random Forest Hyperparameters
    100 xp
    Hyperparameters of KNN
    100 xp
    Setting & Analyzing Hyperparameter Values
    50 xp
    Automating Hyperparameter Choice
    100 xp
    Building Learning Curves
    100 xp
  2. 2

    Grid search

    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.

    Reproduzir Capítulo Agora
  3. 3

    Random Search

    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.

    Reproduzir Capítulo Agora
  4. 4

    Informed Search

    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.

    Reproduzir Capítulo Agora
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conjuntos de dados

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colaboradores

Collaborator's avatar
Hadrien Lacroix
Collaborator's avatar
Chester Ismay
Alex Scriven HeadshotAlex Scriven

Senior Data Scientist @ Atlassian

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