Skip to main content
HomeR

Nonlinear Modeling with Generalized Additive Models (GAMs) in R

GAMs model relationships in data as nonlinear functions that are highly adaptable to different types of data science problems.

Start Course for Free
4 hours15 videos50 exercises8,231 learnersTrophyStatement of Accomplishment

Create Your Free Account

GoogleLinkedInFacebook

or

By continuing, you accept our Terms of Use, our Privacy Policy and that your data is stored in the USA.
Group

Training 2 or more people?

Try DataCamp for Business

Loved by learners at thousands of companies


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.
For Business

Training 2 or more people?

Get your team access to the full DataCamp platform, including all the features.
DataCamp for BusinessFor a bespoke solution book a demo.
  1. 1

    Introduction to Generalized Additive Models

    Free

    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.

    Play Chapter Now
    Introduction
    50 xp
    Motorcycle crash data: linear approach
    100 xp
    Motorcycle crash data: non-linear approach
    100 xp
    Parts of non-linear function
    100 xp
    Basis functions and smoothing
    50 xp
    Setting complexity of the motorcycle model
    100 xp
    Using smoothing parameters to avoid overfitting
    100 xp
    Complexity and smoothing together
    100 xp
    Multivariate GAMs
    50 xp
    Multivariate GAMs of auto performance
    100 xp
    Adding categorical to the auto performance model
    100 xp
    Category-level smooths for different auto types
    100 xp
  2. 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.

    Play Chapter Now
  3. 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.

    Play Chapter Now
  4. 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.

    Play Chapter Now
For Business

Training 2 or more people?

Get your team access to the full DataCamp platform, including all the features.

datasets

Insurance (csale) data

collaborators

Collaborator's avatar
Chester Ismay
Collaborator's avatar
Rasmus Bååth
Collaborator's avatar
Sumedh Panchadhar
Collaborator's avatar
Eunkyung Park
DataCamp Content Creator

Course Instructor

DataCamp offers interactive R, Python, Spreadsheets, SQL and shell courses. All on topics in data science, statistics, and machine learning. Learn from a team of expert teachers in the comfort of your browser with video lessons and fun coding challenges and projects.
See More

What do other learners have to say?

Join over 15 million learners and start Nonlinear Modeling with Generalized Additive Models (GAMs) in R today!

Create Your Free Account

GoogleLinkedInFacebook

or

By continuing, you accept our Terms of Use, our Privacy Policy and that your data is stored in the USA.