Feature Engineering Made Easy
暫譯: 特徵工程簡明指南

Sinan Ozdemir, Divya Susarla

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商品描述

A perfect guide to speed up the predicting power of machine learning algorithms

Key Features

  • Design, discover, and create dynamic, efficient features for your machine learning application
  • Understand your data in-depth and derive astonishing data insights with the help of this Guide
  • Grasp powerful feature-engineering techniques and build machine learning systems

Book Description

Feature engineering is the most important step in creating powerful machine learning systems. This book will take you through the entire feature-engineering journey to make your machine learning much more systematic and effective.

You will start with understanding your data―often the success of your ML models depends on how you leverage different feature types, such as continuous, categorical, and more, You will learn when to include a feature, when to omit it, and why, all by understanding error analysis and the acceptability of your models. You will learn to convert a problem statement into useful new features. You will learn to deliver features driven by business needs as well as mathematical insights. You'll also learn how to use machine learning on your machines, automatically learning amazing features for your data.

By the end of the book, you will become proficient in Feature Selection, Feature Learning, and Feature Optimization.

What you will learn

  • Identify and leverage different feature types
  • Clean features in data to improve predictive power
  • Understand why and how to perform feature selection, and model error analysis
  • Leverage domain knowledge to construct new features
  • Deliver features based on

商品描述(中文翻譯)

加速機器學習演算法預測能力的完美指南

主要特點


  • 設計、發現並創建動態、高效的特徵以應用於您的機器學習應用程式

  • 深入理解您的數據,並透過本指南獲得驚人的數據洞察

  • 掌握強大的特徵工程技術,構建機器學習系統

書籍描述

特徵工程是創建強大機器學習系統中最重要的一步。本書將帶您走過整個特徵工程的旅程,使您的機器學習變得更加系統化和有效。

您將從理解數據開始——通常,您的機器學習模型的成功取決於您如何利用不同的特徵類型,例如連續型、類別型等。您將學習何時應該包含特徵、何時應該省略特徵以及原因,這一切都將通過理解錯誤分析和模型的可接受性來實現。您將學會將問題陳述轉換為有用的新特徵。您將學會根據業務需求和數學洞察來提供特徵。您還將學會如何在您的機器上使用機器學習,自動學習數據的驚人特徵。

在書籍結束時,您將熟練掌握特徵選擇、特徵學習和特徵優化。

您將學到的內容


  • 識別並利用不同的特徵類型

  • 清理數據中的特徵以提高預測能力

  • 理解為什麼以及如何執行特徵選擇和模型錯誤分析

  • 利用領域知識構建新特徵

  • 根據