Memvisualisasikan batas keputusan dan margin

Pada latihan sebelumnya, Anda membangun dua pengklasifikasi linear untuk himpunan data yang dapat dipisahkan secara linear: satu dengan cost = 1 dan satu lagi dengan cost = 100. Pada latihan ini, Anda akan memvisualisasikan margin untuk kedua pengklasifikasi tersebut dalam satu plot. Objek-objek berikut tersedia untuk digunakan:

Himpunan data pelatihan: trainset.
Pengklasifikasi dengan cost = 1 dan cost = 100 masing-masing ada di svm_model_1 dan svm_model_100.
Kemiringan (slope) dan intersep untuk pengklasifikasi cost = 1 disimpan di slope_1 dan intercept_1.
Kemiringan (slope) dan intersep untuk pengklasifikasi cost = 100 disimpan di slope_100 dan intercept_100.
Vektor bobot untuk kedua nilai cost masing-masing disimpan di w_1 dan w_100
Plot sebar dasar dari data pelatihan disimpan di train_plot

Pustaka ggplot2 sudah dimuat sebelumnya.

Latihan ini adalah bagian dari kursus

Support Vector Machines di R

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

#add decision boundary and margins for cost = 1 to training data scatter plot
train_plot_with_margins <- train_plot + 
    geom_abline(slope = ___, intercept = ___) +
    geom_abline(slope = ___, intercept = ___-1/w_1[2], linetype = "dashed")+
    geom_abline(slope = ___, intercept = ___+1/w_1[2], linetype = "dashed")

#display plot
train_plot_with_margins

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: Support Vector Machine Linear Exercise 2: Membuat himpunan data pelatihan dan pengujian Exercise 3: Membangun pengklasifikasi SVM linear Exercise 4: Mengeksplorasi model dan menghitung akurasi Exercise 5: Memvisualisasikan SVM Linear Exercise 6: Memvisualisasikan support vector dengan ggplot Exercise 7: Memvisualisasikan batas keputusan & margin menggunakan `ggplot2`Exercise 8: Memvisualisasikan batas keputusan & margin menggunakan `plot()`Exercise 9: Mengatur parameter SVM linear Exercise 10: Menyetel SVM linear Exercise 11: Memvisualisasikan batas keputusan dan margin

Latihan Saat Ini

Exercise 12: Kapan pengklasifikasi soft margin berguna?Exercise 13: Masalah multiclass Exercise 14: Masalah klasifikasi multikelas Exercise 15: Iris ulang - akurasi yang lebih tangguh.

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