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Mastering Machine Learning with R - Second Edition.
Master machine learning techniques with R to deliver insights in complex projects About This Book Understand and apply machine learning methods using an extensive set of R packages such as XGBOOST Understand the benefits and potential pitfalls of using machine learning methods such as Multi-Class Cl...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Birmingham :
Packt Publishing,
2017.
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| Edición: | 2nd ed. |
| Temas: | |
| Acceso en línea: | Texto completo |
Tabla de Contenidos:
- Cover; Credits; About the Author; About the Reviewers; Packt Upsell; Customer Feedback; Table of Contents; Preface; Chapter 1: A Process for Success; The process; Business understanding; Identifying the business objective; Assessing the situation; Determining the analytical goals; Producing a project plan; Data understanding; Data preparation; Modeling; Evaluation; Deployment; Algorithm flowchart; Summary; Chapter 2: Linear Regression
- The Blocking and Tackling of Machine Learning; Univariate linear regression; Business understanding; Multivariate linear regression; Business understanding.
- Data understanding and preparationModeling and evaluation; Other linear model considerations; Qualitative features; Interaction terms; Summary; Chapter 3: Logistic Regression and Discriminant Analysis; Classification methods and linear regression; Logistic regression; Business understanding; Data understanding and preparation; Modeling and evaluation; The logistic regression model; Logistic regression with cross-validation; Discriminant analysis overview; Discriminant analysis application; Multivariate Adaptive Regression Splines (MARS); Model selection; Summary.
- Chapter 4: Advanced Feature Selection in Linear ModelsRegularization in a nutshell; Ridge regression; LASSO; Elastic net; Business case; Business understanding; Data understanding and preparation; Modeling and evaluation; Best subsets; Ridge regression; LASSO; Elastic net; Cross-validation with glmnet; Model selection; Regularization and classification; Logistic regression example ; Summary; Chapter 5: More Classification Techniques
- K-Nearest Neighbors and Support Vector Machines; K-nearest neighbors; Support vector machines; Business case; Business understanding.
- Data understanding and preparationModeling and evaluation; KNN modeling; SVM modeling; Model selection; Feature selection for SVMs; Summary; Chapter 6: Classification and Regression Trees; An overview of the techniques; Understanding the regression trees; Classification trees; Random forest; Gradient boosting; Business case; Modeling and evaluation; Regression tree; Classification tree; Random forest regression; Random forest classification; Extreme gradient boosting
- classification; Model selection; Feature Selection with random forests; Summary; Chapter 7: Neural Networks and Deep Learning.
- Introduction to neural networksDeep learning, a not-so-deep overview; Deep learning resources and advanced methods; Business understanding; Data understanding and preparation; Modeling and evaluation; An example of deep learning; H2O background; Data upload to H2O; Create train and test datasets; Modeling; Summary; Chapter 8: Cluster Analysis; Hierarchical clustering; Distance calculations; K-means clustering; Gower and partitioning around medoids; Gower; PAM; Random forest; Business understanding; Data understanding and preparation; Modeling and evaluation; Hierarchical clustering.