Bộ sách luyện thi ETS 2023 LC&RC
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When machine learning engineers work with data sets,
they may find the results aren't as good as they need. Instead of
improving the model or collecting more data, they can use the feature
engineering process to help improve results by modifying the data's
features to better capture the nature of the problem. This practical
guide to feature engineering is an essential addition to any data
scientist's or machine learning engineer's toolbox, providing new ideas
on how to improve the performance of a machine learning solution.
Beginning with the basic concepts and techniques, the text builds up to a
unique cross-domain approach that spans data on graphs, texts, time
series, and images, with fully worked out case studies. Key topics
include binning, out-of-fold estimation, feature selection,
dimensionality reduction, and encoding variable-length data. The full
source code for the case studies is available on a companion website as
Python Jupyter notebooks.
Categories:Computers - Computer Science
Year:2020
Edition:1
Language:english
Pages:283
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