Regression and Machine Learning for Education Sciences Using R
portes grátis
Regression and Machine Learning for Education Sciences Using R
Dingsen, Cody
Taylor & Francis Ltd
11/2024
360
Dura
9781032510088
Pré-lançamento - envio 15 a 20 dias após a sua edição
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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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Machine Learning;Regression Modeling;Plus Factor;Cluster Analysis Methods;R Software;Data Analytics;Data Science;Predictive Modeling and Classification;Visualisation
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
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
