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.
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.Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
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
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
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