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Feature Selection in Machine Learning. 20+ feature selection methods to build simpler, faster, and more reliable ML models Soledad Galli, PhD

Książka w języku angielskim angielski
Feature Selection in Machine Learning. 20+ feature selection methods to build simpler, faster, and more reliable ML models Soledad Galli, PhD - okladka książki

Feature Selection in Machine Learning. 20+ feature selection methods to build simpler, faster, and more reliable ML models Soledad Galli, PhD - okladka książki

Feature Selection in Machine Learning. 20+ feature selection methods to build simpler, faster, and more reliable ML models Soledad Galli, PhD - audiobook MP3

Feature Selection in Machine Learning. 20+ feature selection methods to build simpler, faster, and more reliable ML models Soledad Galli, PhD - audiobook CD

Autorzy:
Soledad Galli, PhD
Wydawnictwo:
Packt Publishing (Z chęcią przeczytam książkę w języku polskim)
Ocena:
Bądź pierwszym, który oceni tę książkę
Stron:
216
Dostępne formaty:
     PDF
     ePub

Ebook
98,10 zł 109,00 zł (-10%)
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Do przechowalni

Feature selection is essential for building efficient machine learning models, yet choosing the right variables can be challenging. Irrelevant, redundant, and highly correlated features increase complexity, slow training, and can affect model performance and interpretability
Feature Selection in Machine Learning provides a practical guide to identifying the most predictive variables using Python. Starting with the fundamentals, you'll learn why feature selection matters and how different methods help simplify datasets without losing valuable information
You'll begin by removing constant, quasi-constant, duplicate, and correlated features before exploring statistical techniques such as chi-square, ANOVA, and mutual information. The book then introduces univariate selection, wrapper methods, embedded techniques using Lasso and decision trees, and recursive feature elimination. You'll also explore alternative approaches, including feature permutation, minimum redundancy maximum relevance (MRMR), and Boruta
With practical code examples and open-source Python libraries, you'll learn to compare techniques and select the right approach for your data and models. By the end, you'll be equipped to build simpler, faster, and more interpretable machine learning solutions

O autorze książki

Soledad Galli is a lead data scientist with more than 10 years of experience in world-class academic institutions and renowned businesses. She has researched, developed, and put into production machine learning models for insurance claims, credit risk assessment, and fraud prevention. Soledad received a Data Science Leaders' award in 2018 and was named one of LinkedIn's voices in data science and analytics in 2019. She is passionate about enabling people to step into and excel in data science, which is why she mentors data scientists and speaks at data science meetings regularly. She also teaches online courses on machine learning in a prestigious Massive Open Online Course platform, which have reached more than 10,000 students worldwide.

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