Menggunakan `tune.svm()`

Latihan ini akan memberi Anda pengalaman langsung menggunakan fungsi tune.svm(). Anda akan menggunakannya untuk memperoleh nilai optimal bagi parameter cost, gamma, dan coef0 untuk model SVM berdasarkan himpunan data yang dapat dipisahkan secara radial yang Anda buat sebelumnya pada bab ini. Data latih tersedia dalam dataframe trainset, data uji dalam testset, dan pustaka e1071 telah dimuat sebelumnya untuk Anda. Ingat bahwa variabel kelas y disimpan pada kolom ketiga dari trainset dan testset.

Ingat juga bahwa dalam video, Kailash menggunakan cost=10^(1:3) untuk mendapatkan rentang parameter cost dari 10=10^1 hingga 1000=10^3 dalam kelipatan 10.

Latihan ini adalah bagian dari kursus

Support Vector Machines di R

Petunjuk latihan

Atur rentang pencarian parameter sebagai berikut:
- cost - dari 0,1 (10^(-1)) hingga 100 (10^2) dalam kelipatan 10.
- gamma dan coef0 - salah satu dari nilai berikut: 0,1; 1; dan 10.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

#tune model
tune_out <- 
    tune.svm(x = trainset[, -3], y = trainset[, 3], 
             type = "C-classification", 
             kernel = "polynomial", degree = 2, cost = 10^(___:___), 
             gamma = c(___, ___, ___), coef0 = c(0.1, 1, 10))

#list optimal values
tune_out$best.parameters$___
tune_out$best.parameters$___
tune_out$best.parameters$___

Edit dan Jalankan Kode

Latihan ini adalah bagian dari kursus

Support Vector Machines di R

SkillTag.level.intermediateSkillTag.label

4.9+

Mulai Kursus Gratis

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: Membuat himpunan data yang dapat dipisahkan secara radial Exercise 2: Membuat himpunan data 2D yang dapat dipisahkan secara radial Exercise 3: Memvisualisasikan himpunan data Exercise 4: SVM Linear pada data yang terpisah secara radial Exercise 5: SVM linear untuk himpunan data yang terpisah radial Exercise 6: Akurasi rata-rata untuk SVM linear Exercise 7: Trik kernel Exercise 8: Memvisualisasikan data tertransformasi yang dapat dipisahkan radial Exercise 9: SVM dengan kernel polinomial Exercise 10: Menyetel SVM Exercise 11: Menggunakan `tune.svm()`

Latihan Saat Ini

Exercise 12: Membangun dan memvisualisasikan model yang telah dituning

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