Cody Dingsen

Regression and Machine Learning for Education Sciences Using R (eBook, ePUB)

• Format: ePub

Produktbeschreibung

This book provides a conceptual introduction to regression and machine learning and its applications in education research. The book discusses its diverse applications, including its role in predicting future events based on the current data or explaining why some phenomena occur.

---

Dieser Download kann aus rechtlichen Gründen nur mit Rechnungsadresse in A, B, BG, CY, CZ, D, DK, EW, E, FIN, F, GR, HR, H, IRL, I, LT, L, LR, M, NL, PL, P, R, S, SLO, SK ausgeliefert werden.

Produktdetails

• Verlag: Taylor & Francis
• Erscheinungstermin: 1. November 2024
• Englisch
• ISBN-13: 9781040145067
• Artikelnr.: 72285342

Autorenporträt

Cody Dingsen is a professor in the Department of Educational Sciences and Professional Programs at the University of Missouri-St. Louis, Missouri, USA. His research interests include multidimensional scaling models for change and preference, psychometrics, data science, cognition and learning, emotional development, and biopsychosocial development.

Inhaltsangabe

A brief introduction to R and R Studio Part 1: Regression models:
foundation of machine learning Chapter 01: First thing first: simple
regression Chapter 02: Beyond simple: multiple regression analysis Chapter
03: It takes two to tangle: regression with interactions Chapter 04: Are we
thinking correctly? Checking assumptions of regression model Chapter 05: I
am not straight but robust: curvilinear Robust and quantile regression
Chapter 06: Predicting the class probability: logistic regression model
Part 2: Machine learning: classification and predictive modeling Chapter
07: Introduction to machine learning Chapter 08. Machine learning
algorithms and process Chapter 09. Let me regulate: regularized machine
learning Chapter 10. Finding ways in the forest: prediction with random
forest Chapter 11. I can divide better: classification with support vector
machine Chapter 12. Work like a human brain: artificial neural network
Chapter 13. Desire to find causal relations: bayesian network Chapter 14.
We want to see the relationships: multivariate data visualization