Modeli keşfetme ve doğruluğu hesaplama

Bu egzersizde modelin içeriğini keşfedecek ve eğitim ile test doğruluklarını hesaplayacaksın. Eğitim ve test verileri sırasıyla trainset ve testset veri çerçevelerinde mevcut, SVM modeli ise svm_model değişkeninde saklı.

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R ile Support Vector Machines

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Bu egzersiz

R ile Support Vector Machines

kursunun bir parçasıdır

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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: Doğrusal Destek Vektör Makineleri Exercise 2: Eğitim ve test veri kümeleri oluşturma Exercise 3: Doğrusal bir SVM sınıflandırıcısı oluşturma Exercise 4: Modeli keşfetme ve doğruluğu hesaplama

Geçerli Egzersiz

Exercise 5: Lineer SVM'leri Görselleştirme Exercise 6: ggplot ile destek vektörlerini görselleştirme Exercise 7: `ggplot2` kullanarak karar ve marj sınırlarını görselleştirme Exercise 8: Karar ve marj sınırlarını `plot()` ile görselleştirme Exercise 9: Doğrusal SVM’leri ayarlama Exercise 10: Doğrusal bir SVM'i ayarlama Exercise 11: Karar sınırlarını ve marjinleri görselleştirme Exercise 12: Yumuşak marjin sınıflandırıcılar ne zaman kullanışlıdır?Exercise 13: Çok sınıflı problemler Exercise 14: Çok sınıflı bir sınıflandırma problemi Exercise 15: Iris yeniden: daha sağlam bir doğruluk.

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: 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