Nouveauté
Data Science through R. Unsupervised Learning. Dimension Reduction Techniques: Principal Components, Factor Analysis and Correspondence Analysis
Par :Formats :
Disponible dans votre compte client Decitre ou Furet du Nord dès validation de votre commande. Le format ePub est :
- Compatible avec une lecture sur My Vivlio (smartphone, tablette, ordinateur)
- Compatible avec une lecture sur liseuses Vivlio
- Pour les liseuses autres que Vivlio, vous devez utiliser le logiciel Adobe Digital Edition. Non compatible avec la lecture sur les liseuses Kindle, Remarkable et Sony
, qui est-ce ?Notre partenaire de plateforme de lecture numérique où vous retrouverez l'ensemble de vos ebooks gratuitement
Pour en savoir plus sur nos ebooks, consultez notre aide en ligne ici
C'est si simple ! Lisez votre ebook avec l'app Vivlio sur votre tablette, mobile ou ordinateur :
- FormatePub
- ISBN8231619542
- EAN9798231619542
- Date de parution26/07/2025
- Protection num.pas de protection
- Infos supplémentairesepub
- ÉditeurWalzone Press
Résumé
Data science algorithms use computational methods to extract information directly from data. Machine learning uses two types of techniques: supervised learning, which trains a model with known input and output data so that it can predict future outcomes, and unsupervised learning, which finds hidden patterns or intrinsic structures in the input data. Most of the unsupervised analysis techniques related to dimension reduction are developed throughout this book from a methodological and practical perspective with applications through the R software.
The following techniques are explored in depth: Principal Components Analysis, Factor Analysis, Simple Correspondence Analysis, and Multiple Correspondence Analysis.
The following techniques are explored in depth: Principal Components Analysis, Factor Analysis, Simple Correspondence Analysis, and Multiple Correspondence Analysis.
Data science algorithms use computational methods to extract information directly from data. Machine learning uses two types of techniques: supervised learning, which trains a model with known input and output data so that it can predict future outcomes, and unsupervised learning, which finds hidden patterns or intrinsic structures in the input data. Most of the unsupervised analysis techniques related to dimension reduction are developed throughout this book from a methodological and practical perspective with applications through the R software.
The following techniques are explored in depth: Principal Components Analysis, Factor Analysis, Simple Correspondence Analysis, and Multiple Correspondence Analysis.
The following techniques are explored in depth: Principal Components Analysis, Factor Analysis, Simple Correspondence Analysis, and Multiple Correspondence Analysis.
