Radyal olarak ayrılabilir bir veri kümesi için lineer SVM

Bu egzersizde, bu bölümün ilk dersinde oluşturduğun radyal olarak ayrılabilir veri kümesi için iki lineer SVM kuracaksın: biri cost = 1 (varsayılan), diğeri cost = 100. Ayrıca her iki cost değeri için eğitim ve test doğruluklarını hesaplayacaksın. e1071 kütüphanesi yüklendi ve eğitim ile test veri kümeleri senin için oluşturuldu; trainset ve testset veri çerçevelerinde hazırlar.

Bu egzersiz

R ile Support Vector Machines

kursunun bir parçasıdır

Kursu Görüntüle

Uygulamalı interaktif egzersiz

Bu örnek kodu tamamlayarak bu egzersizi bitirin.

#default cost mode;
svm_model_1 <- svm(y ~ ., data = ___, type = "C-classification", cost = ___, kernel = "linear")

#training accuracy
pred_train <- predict(svm_model_1, ___)
mean(pred_train == ___$y)

#test accuracy
pred_test <- predict(svm_model_1, ___)
mean(pred_test == ___$y)

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: Radyal olarak ayrılabilir bir veri kümesi oluşturma Exercise 2: 2B radyal olarak ayrılabilir bir veri kümesi oluşturma Exercise 3: Veri kümesini görselleştirme Exercise 4: Radyal olarak ayrılabilir veride doğrusal SVM'ler Exercise 5: Radyal olarak ayrılabilir bir veri kümesi için lineer SVM

Geçerli Egzersiz

Exercise 6: Doğrusal SVM için ortalama doğruluk Exercise 7: Çekirdek hilesi Exercise 8: Dönüştürülmüş radyal olarak ayrılabilir veriyi görselleştirme Exercise 9: Polinom çekirdekli SVM Exercise 10: SVM'leri Ayarlama Exercise 11: `tune.svm()` kullanma Exercise 12: Ayarlanmış modeli kurma ve görselleştirme

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: Generating a complex dataset Exercise 2: Generating a complex dataset - part 1 Exercise 3: Generating a complex dataset - part 2 Exercise 4: Visualizing the dataset Exercise 5: Motivating the RBF kernel Exercise 6: Linear SVM for complex dataset Exercise 7: Quadratic SVM for complex dataset Exercise 8: The RBF Kernel Exercise 9: Polynomial SVM on a complex dataset Exercise 10: RBF SVM on a complex dataset Exercise 11: Tuning an RBF kernel SVM