Farklı araç tipleri için kategori düzeyinde düzgünlükler

Şimdi modellerini, kategorik terimlerin farklı düzeyleri için farklı düzgünlükler içerecek şekilde genişleteceksin.

Bu egzersiz

R ile Genelleştirilmiş Katkılı Modeller (GAM'ler) ile Doğrusal Olmayan Modelle

kursunun bir parçasıdır

Kursu Görüntüle

Egzersiz talimatları

by= argümanını kullanarak her bir düzgünlük terimini drive kategorik değişkenine bağlı hale getirip, weight, length ve price için düzgünlük terimleriyle şehir içi yakıt verimliliğini (city.mpg) tahmin eden bir model kur.
drive değişkeni için ayrıca ayrı bir doğrusal terim ekle.

Uygulamalı interaktif egzersiz

Bu örnek kodu tamamlayarak bu egzersizi bitirin.

library(mgcv)
# Fit the model
mod_city3 <- gam(city.mpg ~ ___, 
                 data = mpg, method = "REML")

# Plot the model
plot(mod_city3, pages = 1)

Kodu Düzenle ve Çalıştır

Bu egzersiz

R ile Genelleştirilmiş Katkılı Modeller (GAM'ler) ile Doğrusal Olmayan Modelle

kursunun bir parçasıdır

IntermediárioNível de habilidade

4.8+

Kursa Ücretsiz Başlayın

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.

Exercise 1: Giriş Exercise 2: Motosiklet kaza verisi: doğrusal yaklaşım Exercise 3: Motosiklet kaza verileri: doğrusal olmayan yaklaşım Exercise 4: Doğrusal olmayan fonksiyonun parçaları Exercise 5: Temel fonksiyonlar ve yumuşatma Exercise 6: Motosiklet modelinin karmaşıklığını ayarlama Exercise 7: Aşırı uyumu önlemek için düzeltme parametrelerini kullanma Exercise 8: Karmaşıklık ve düzeltmeyi birlikte kullanma Exercise 9: Çok Değişkenli GAM'ler Exercise 10: Otomobil performansının çok değişkenli GAM'leri Exercise 11: Oto performans modeline kategorik değişken ekleme Exercise 12: Farklı araç tipleri için kategori düzeyinde düzgünlükler

Geçerli Egzersiz

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.

Exercise 1: Interpreting GAM outputs Exercise 2: Significance and linearity (I)Exercise 3: Significance and linearity (II)Exercise 4: Visualizing GAMs Exercise 5: Plotting the motorcycle crash model and data Exercise 6: Plotting multiple auto performance variables Exercise 7: Visualizing auto performance uncertainty Exercise 8: Model checking with gam.check()Exercise 9: Reading model diagnostics Exercise 10: Fixing problems with model diagnostics Exercise 11: Checking concurvity Exercise 12: Examining overall concurvity in auto data Exercise 13: Examining concurvity between auto variables

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.

Exercise 1: Two-dimensional smooths and spatial data Exercise 2: Modeling soil pollution in the Netherlands Exercise 3: Adding more variables to predict soil pollution Exercise 4: Plotting and interpreting GAM interactions Exercise 5: Plotting the model surface Exercise 6: Customizing 3D plots Exercise 7: Extrapolation in surface plots Exercise 8: Visualizing categorical-continuous interactions Exercise 9: Soil pollution in different land uses Exercise 10: Plotting pollution in different land uses Exercise 11: Interactions with different scales: Tensors Exercise 12: Pollution models with multi-scale interactions Exercise 13: Teasing apart multi-scale interactions

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.

Exercise 1: Types of model outcomes Exercise 2: Classifying purchasing behavior Exercise 3: Purchase behavior with multiple smooths Exercise 4: Visualizing logistic GAMs Exercise 5: Visualizing influences on purchase probability Exercise 6: Interpreting purchase effect plots (I)Exercise 7: Interpreting purchase effect plots (II)Exercise 8: Interpreting purchase effect plots (III)Exercise 9: Making predictions Exercise 10: Predicting purchase behavior and uncertainty Exercise 11: Explaining individual behaviors Exercise 12: Doing more with GAMs