Extract accurate information from data to train and improve machine learning models using NumPy, SciPy, pandas, and scikit-learn libraries

Key Features-:
Discover solutions for feature generation, feature extraction, and feature selection Uncover the end-to-end feature engineering process across continuous, discrete, and unstructured datasets Implement modern feature extraction techniques using Python's pandas, scikit-learn, SciPy and NumPy libraries Book Description

Feature engineering is invaluable for developing and enriching your machine learning models. In this cookbook, you will work with the best tools to streamline your feature engineering pipelines and techniques and simplify and improve the quality of your code.

Using Python libraries such as pandas, scikit-learn, Featuretools, and Feature-engine, you’ll learn how to work with both continuous and discrete datasets and be able to transform features from unstructured datasets. You will develop the skills necessary to select the best features as well as the most suitable extraction techniques. This book will cover Python recipes that will help you automate feature engineering to simplify complex processes. You’ll also get to grips with different feature engineering strategies, such as the box-cox transform, power transform, and log transform across machine learning, reinforcement learning, and natural language processing (NLP) domains.

By the end of this book, you’ll have discovered tips and practical solutions to all of your feature engineering problems.

使用NumPy、SciPy、pandas和scikit-learn库从数据中提取准确的信息来训练和改进机器学习模型

主要特点--。

发现特征生成、特征提取和特征选择的解决方案 揭示跨越连续、离散和非结构化数据集的端到端特征工程过程 使用Python的pandas、scikit-learn、SciPy和NumPy库实现现代特征提取技术 书籍简介

特征工程对于开发和丰富你的机器学习模型是非常宝贵的。在这本食谱中，你将使用最好的工具来简化你的特征工程管道和技术，并简化和提高代码的质量。

使用Python库，如pandas、scikit-learn、Featuretools和Feature-engine，你将学习如何处理连续和离散的数据集，并能够从非结构化数据集中转换特征。你将发展选择最佳特征以及最合适的提取技术的必要技能。本书将涵盖Python配方，帮助你实现特征工程的自动化，以简化复杂的过程。你还会掌握不同的特征工程策略，如横跨机器学习、强化学习和自然语言处理（NLP）领域的箱形变换、功率变换和日志变换。

在本书结束时，你将发现所有特征工程问题的技巧和实用解决方案。