000			a
999			_c33844 _d33844
008			250422b xxu||||| |||| 00| 0 eng d
020			_a9781491953242
082			_a006.31 _bZHE
100			_aZheng, Alice
245			_aFeature engineering for machine learning : principles and techniques for data scientists
260			_bO'Reilly, _c2018 _aBeijing :
300			_axiii, 200 p. ; _bill. (some col.), _c24 cm.
365			_b900.00 _c₹ _d01
504			_aIncludes bibliographical references and index.
520			_aFeature 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.
650			_aData mining
650			_aBox-Cox transform
650			_aCategorical variable
650			_aCollaborative Filtering
650			_aData points
650			_aDummy coding
650			_aFeature space
650			_aImage gradients
650			_aLogistic Regression
650			_aSingular Value Decomposition
700			_aCasari, Amanda
942			_2ddc _cBK