Veri kümesini görselleştirme

Bu egzersizde, önceki egzersizlerde oluşturduğun karmaşık veri kümesini görselleştirmek için ggplot() kullanacaksın. Veri kümesi df veri çerçevesinde mevcut. Karar sınırını görselleştirmen gerekmiyor.

Burada, grafikte x ve y eksenlerine aynı fiziksel gösterimi vermek için coord_fixed() kullanacak ve böylece dairelerin elips yerine daire olarak görünmesini sağlayacaksın.

Bu egzersiz

R ile Support Vector Machines

kursunun bir parçasıdır

Kursu Görüntüle

Egzersiz talimatları

Gerekli grafik kütüphanesini yükle.
aesthetics parametresinin argümanlarını ayarla.
Saçılım grafiği için uygun geom_ fonksiyonunu seç.
Argüman vermeden coord_fixed() ekleyerek eşit koordinatlar belirt.

Uygulamalı interaktif egzersiz

Bu örnek kodu tamamlayarak bu egzersizi bitirin.

# Load ggplot2
library(___)

# Plot x2 vs. x1, colored by y
scatter_plot<- ggplot(data = df, aes(x = ___, y = ___, color = ___)) + 
    # Add a point layer
    geom___() + 
    scale_color_manual(values = c("red", "blue")) +
    # Specify equal coordinates
    coord_fixed()
 
scatter_plot

Kodu Düzenle ve Çalıştır

Bu egzersiz

R ile Support Vector Machines

kursunun bir parçasıdır

IntermediárioNível de habilidade

4.9+

Kursa Ücretsiz Başlayın

This chapter introduces some key concepts of support vector machines through a simple 1-dimensional example. Students are also walked through the creation of a linearly separable dataset that is used in the subsequent chapter.

Exercise 1: Sugar content of soft drinks Exercise 2: Visualizing a sugar content dataset Exercise 3: Identifying decision boundaries Exercise 4: Find the maximal margin separator Exercise 5: Visualize the maximal margin separator Exercise 6: Generating a linearly separable dataset Exercise 7: Generate a 2d uniformly distributed dataset.Exercise 8: Create a decision boundary Exercise 9: Introduce a margin in the dataset

Introduces students to the basic concepts of support vector machines by applying the svm algorithm to a dataset that is linearly separable. Key concepts are illustrated through ggplot visualisations that are built from the outputs of the algorithm and the role of the cost parameter is highlighted via a simple example. The chapter closes with a section on how the algorithm deals with multiclass problems.

Exercise 1: Linear Support Vector Machines Exercise 2: Creating training and test datasets Exercise 3: Building a linear SVM classifier Exercise 4: Exploring the model and calculating accuracy Exercise 5: Visualizing Linear SVMs Exercise 6: Visualizing support vectors using ggplot Exercise 7: Visualizing decision & margin bounds using `ggplot2`Exercise 8: Visualizing decision & margin bounds using `plot()`Exercise 9: Tuning linear SVMs Exercise 10: Tuning a linear SVM Exercise 11: Visualizing decision boundaries and margins Exercise 12: When are soft margin classifiers useful?Exercise 13: Multiclass problems Exercise 14: A multiclass classification problem Exercise 15: Iris redux - a more robust accuracy.

Provides an introduction to polynomial kernels via a dataset that is radially separable (i.e. has a circular decision boundary). After demonstrating the inadequacy of linear kernels for this dataset, students will see how a simple transformation renders the problem linearly separable thus motivating an intuitive discussion of the kernel trick. Students will then apply the polynomial kernel to the dataset and tune the resulting classifier.

Exercise 1: Generating a radially separable dataset Exercise 2: Generating a 2d radially separable dataset Exercise 3: Visualizing the dataset Exercise 4: Linear SVMs on radially separable data Exercise 5: Linear SVM for a radially separable dataset Exercise 6: Average accuracy for linear SVM Exercise 7: The kernel trick Exercise 8: Visualizing transformed radially separable data Exercise 9: SVM with polynomial kernel Exercise 10: Tuning SVMs Exercise 11: Using `tune.svm()`Exercise 12: Building and visualizing the tuned model

Builds on the previous three chapters by introducing the highly flexible Radial Basis Function (RBF) kernel. Students will create a "complex" dataset that shows up the limitations of polynomial kernels. Then, following an intuitive motivation for the RBF kernel, students see how it addresses the shortcomings of the other kernels discussed in this course.

Exercise 1: Karmaşık bir veri kümesi oluşturma Exercise 2: Karmaşık bir veri kümesi oluşturma - bölüm 1 Exercise 3: Karmaşık bir veri kümesi oluşturma - bölüm 2 Exercise 4: Veri kümesini görselleştirme

Geçerli Egzersiz

Exercise 5: RBF çekirdeğine motivasyon Exercise 6: Karmaşık veri kümesi için doğrusal SVM Exercise 7: Karmaşık veri kümesi için ikinci dereceden SVM Exercise 8: RBF Çekirdeği Exercise 9: Karmaşık bir veri kümesinde polinom SVM Exercise 10: Karmaşık bir veri kümesinde RBF SVM Exercise 11: Bir RBF kernel SVM’ini ayarlama