EAN: 9781491953242

FEATURE ENGINEERING FOR MACHINE LEARNING MODELS...

Feature engineering is a crucial step in the machine-learning pipeline  yet this topic is
rarely examined on its own. With this practical book  you'll learn techniques for extracting
and transforming features—the numeric representations of raw data—into formats for
machine-learning models. Each chapter guides you through a single data problem  such as how to
represent text or image data. Together  these examples illustrate the main principles of
feature engineering. Rather than simply teach these principles  authors Alice Zheng and Amanda
Casari focus on practical application with exercises throughout the book. The closing chapter
brings everything together by tackling a real-world  structured dataset with several
feature-engineering techniques. Python packages including numpy  Pandas  Scikit-learn  and
Matplotlib are used in code examples. You'll examine: Feature engineering for numeric data:
filtering  binning  scaling  log transforms  and power transforms Natural text techniques:
bag-of-words  n-grams  and phrase detection Frequency-based filtering and feature scaling for
eliminating uninformative features Encoding techniques of categorical variables  including
feature hashing and bin-counting Model-based feature engineering with principal component
analysis The concept of model stacking  using k-means as a featurization technique Image
feature extraction with manual and deep-learning techniques

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