Book contents
- Frontmatter
- Dedication
- Contents
- Preface
- Part One Fundamentals
- 1 Introduction
- 2 Features, Combined: Normalization, Discretization and Outliers
- 3 Features, Expanded: Computable Features, Imputation and Kernels
- 4 Features, Reduced: Feature Selection, Dimensionality Reduction and Embeddings
- 5 Advanced Topics: Variable-Length Data and Automated Feature Engineering
- Part II Case Studies
- Bibliography
- Index
1 - Introduction
from Part One - Fundamentals
Published online by Cambridge University Press: 29 May 2020
- Frontmatter
- Dedication
- Contents
- Preface
- Part One Fundamentals
- 1 Introduction
- 2 Features, Combined: Normalization, Discretization and Outliers
- 3 Features, Expanded: Computable Features, Imputation and Kernels
- 4 Features, Reduced: Feature Selection, Dimensionality Reduction and Embeddings
- 5 Advanced Topics: Variable-Length Data and Automated Feature Engineering
- Part II Case Studies
- Bibliography
- Index
Summary
This chapter starts with basic definitions such as types of machine learning (supervised vs. unsupervised learning, classifiers vs. regressors), types of features (binary, categorical, discrete, continuos), metrics (precision, recall, f-measure, accuracy, overfitting), and raw data and then defines the machine learning cycle and the feature engineering cycle. The feature engineering cycle hinges on two types of analysis: exploratory data analysis, at the beginning of the cycle and error analysis at the end of each feature engineering cycle. Domain modelling and feature construction concludes the chapter with particular emphasis on feature ideation techniques.
Keywords
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- The Art of Feature EngineeringEssentials for Machine Learning, pp. 3 - 33Publisher: Cambridge University PressPrint publication year: 2020