Hyperparameter Tuning with Python: Boost your machine learning model's performance via hyperparameter tuning

Take your machine learning models to the next level by learning how to leverage hyperparameter tuning, allowing you to control the model's finest details

Key Features:

• Gain a deep understanding of how hyperparameter tuning works
• Explore exhaustive search, heuristic search, and Bayesian and multi-fidelity optimization methods
• Learn which method should be used to solve a specific situation or problem

Book Description:

Hyperparameters are an important element in building useful machine learning models. This book curates numerous hyperparameter tuning methods for Python, one of the most popular coding languages for machine learning. Alongside in-depth explanations of how each method works, you will use a decision map that can help you identify the best tuning method for your requirements.

You'll start with an introduction to hyperparameter tuning and understand why it's important. Next, you'll learn the best methods for hyperparameter tuning for a variety of use cases and specific algorithm types. This book will not only cover the usual grid or random search but also other powerful underdog methods. Individual chapters are also dedicated to the three main groups of hyperparameter tuning methods: exhaustive search, heuristic search, Bayesian optimization, and multi-fidelity optimization. Later, you will learn about top frameworks like Scikit, Hyperopt, Optuna, NNI, and DEAP to implement hyperparameter tuning. Finally, you will cover hyperparameters of popular algorithms and best practices that will help you efficiently tune your hyperparameter.

By the end of this book, you will have the skills you need to take full control over your machine learning models and get the best models for the best results.

What You Will Learn:

• Discover hyperparameter space and types of hyperparameter distributions
• Explore manual, grid, and random search, and the pros and cons of each
• Understand powerful underdog methods along with best practices
• Explore the hyperparameters of popular algorithms
• Discover how to tune hyperparameters in different frameworks and libraries
• Deep dive into top frameworks such as Scikit, Hyperopt, Optuna, NNI, and DEAP
• Get to grips with best practices that you can apply to your machine learning models right away

Who this book is for:

This book is for data scientists and ML engineers who are working with Python and want to further boost their ML model's performance by using the appropriate hyperparameter tuning method. Although a basic understanding of machine learning and how to code in Python is needed, no prior knowledge of hyperparameter tuning in Python is required.

將機器學習模型提升到更高水平，學習如何利用超參數調整來控制模型的細節。

主要特點：
- 深入了解超參數調整的工作原理
- 探索穷盡搜索、啟發式搜索、貝葉斯和多重精度優化方法
- 學習在特定情況或問題中應該使用哪種方法解決

書籍描述：
超參數是構建有用的機器學習模型的重要元素。本書整理了Python的多種超參數調整方法，Python是最受歡迎的機器學習編程語言之一。除了深入解釋每種方法的工作原理外，您還將使用一個決策圖，幫助您確定最適合您需求的調整方法。

您將從超參數調整的介紹開始，並了解其重要性。接下來，您將學習各種用例和特定算法類型的超參數調整的最佳方法。本書不僅涵蓋了常見的網格或隨機搜索，還介紹了其他強大的方法。各個章節還專門介紹了三個主要超參數調整方法的群組：穷盡搜索、啟發式搜索、貝葉斯優化和多重精度優化。隨後，您將了解Scikit、Hyperopt、Optuna、NNI和DEAP等頂級框架，以實現超參數調整。最後，您將涵蓋流行算法的超參數和最佳實踐，這將幫助您高效調整超參數。

通過閱讀本書，您將掌握完全控制機器學習模型並獲得最佳結果的技能。

學到什麼：
- 探索超參數空間和超參數分佈的類型
- 探索手動、網格和隨機搜索，以及每種方法的優缺點
- 了解強大的方法以及最佳實踐
- 探索流行算法的超參數
- 發現如何在不同的框架和庫中調整超參數
- 深入研究Scikit、Hyperopt、Optuna、NNI和DEAP等頂級框架
- 掌握可立即應用於機器學習模型的最佳實踐

本書適合對Python有基本了解並希望通過適當的超參數調整方法進一步提升機器學習模型性能的數據科學家和機器學習工程師。雖然需要對機器學習和Python編程有基本理解，但不需要事先了解Python中的超參數調整。