Machine Learning with R, the tidyverse, and mlr
Machine Learning with R, the tidyverse, and mlr gets you started in machine learning using R Studio and the awesome mlr machine learning package. This practical guide simplifies theory and avoids needlessly complicated statistics or math. All core ML techniques are clearly explained through graphics...
| Otros Autores: | |
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| Formato: | Libro electrónico |
| Idioma: | Inglés |
| Publicado: |
Shelter Island, New York :
Manning
[2020]
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| Edición: | 1st edition |
| Materias: | |
| Ver en Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009630920806719 |
Tabla de Contenidos:
- Part 1. Introduction. Introduction to machine learning
- Tidying manipulating and plotting data with the tidyverse
- Part 2. Classification. Classifying based on similarities with k-nearest neighbors
- Classifying based on odds with logistic regression
- Classifying by maximizing separation with discriminant analysis
- Classifying with naive Bayes and support vector machines
- Classifying with decision trees
- Improving decision trees with random forests and boosting
- Part 3. Regression. Linear regression
- Nonlinear regression with generalized additive models
- Preventing overfitting with ridge regression, LASSO, and elastic net
- Regression with kNN, random forest, and XGBoost
- Part 4. Dimension reduction. Maximizing variance with principal component analysis
- Maximizing similarity with t-SNE and UMAP
- Self-organizing maps, and locally linear embedding
- Part 5. Clustering. Clustering by finding centers with k-means
- Hierarchical clustering
- Clustering based on density: DBSCAN and OPTICS
- Clustering based on distributions with mixture modeling
- Final notes and further reading.
