After doing these courses, I feel confident creating professional visualizations and dashboards
Shoebur RahmanProcurement Specialist, Giz Bangladesh
Course Description
Generalized Additive Models are a powerful tool for both prediction and inference. More flexible than linear models, and more understandable than black-box methods, GAMs model relationships in data as nonlinear functions that are highly adaptable to different types of data and data science problems. In this course, you'll learn how GAMs work and how to construct them with the popular mgcv package. You'll learn how to interpret, explain and visualize your model results, and how to diagnose and fix model problems. You'll work with data sets that will show you how to apply GAMs to a variety of situations: automobile performance data for building mixed linear and nonlinear models, soil pollution data for building geospatial models, and consumer purchasing data for classification and prediction. By the end of this course, you'll have a toolbox for solving many data science problems.
Prerequisites
Curriculum
Course outline
1
Introduction to Generalized Additive Models
In this chapter, you will learn how Generalized additive models work and how to use flexible, nonlinear functions to model data without over-fitting. You will learn to use the gam() function in the mgcv package, and how to build multivariate models that mix nonlinear, linear, and categorical effects to data.
- Introduction50 XP
- Motorcycle crash data: linear approach100 XP
- Motorcycle crash data: non-linear approach100 XP
- Parts of non-linear function100 XP
- Basis functions and smoothing50 XP
- Setting complexity of the motorcycle model100 XP
- Using smoothing parameters to avoid overfitting100 XP
- Complexity and smoothing together100 XP
- Multivariate GAMs50 XP
- Multivariate GAMs of auto performance100 XP
- Adding categorical to the auto performance model100 XP
- Category-level smooths for different auto types100 XP
2
Interpreting and Visualizing GAMs
In this chapter, you will take a closer look at the models you fit in chapter 1 and learn how to interpret and explain them. You will learn how to make plots that show how different variables affect model outcomes. Then you will diagnose problems in models arising from under-fitting the data or hidden relationships between variables, and how to iteratively fix those problems and get better results.
3
Spatial GAMs and Interactions
In this chapter, you will extend the types of models you can fit to those with interactions of multiple variables. You will fit models of geospatial data by using these interactions to model complex surfaces, and visualize those surfaces in 3D. Then you will learn about interactions between smooth and categorical variables, and how to model interactions between very different variables like space and time.
4
Logistic GAMs for Classification
In the first three chapters, you used GAMs for regression of continuous outcomes. In this chapter, you will use GAMs for classification. You will build logistic GAMs to predict binary outcomes like customer purchasing behavior, learn to visualize this new type of model, make predictions, and learn how to explain the variables that influence each prediction.
R
Nonlinear Modeling with Generalized Additive Models (GAMs) in R
Course
Complete
