Temukan pemisah margin maksimal

Ingat bahwa himpunan data yang kita gunakan berisi pengukuran kadar gula dari 25 sampel acak dua minuman ringan, satu reguler dan satu lagi dengan gula berkurang. Pada salah satu plot sebelumnya, kita mengidentifikasi dua klaster (kelas) yang berbeda. Himpunan data di mana kelas-kelasnya tidak saling tumpang tindih disebut terpisahkan (separable), dengan kelas-kelas dipisahkan oleh decision boundary. Maximal margin separator adalah decision boundary yang jaraknya paling jauh dari kedua kelas. Letaknya pada rata-rata dari titik-titik ekstrem yang relevan dari masing-masing kelas. Dalam kasus ini, titik yang relevan adalah titik bernilai tertinggi pada kelas kadar gula rendah dan titik bernilai terendah pada kelas kadar gula tinggi. Latihan ini meminta Anda menemukan maximal margin separator untuk himpunan data kadar gula.

Latihan ini adalah bagian dari kursus

Support Vector Machines di R

Petunjuk latihan

Temukan maximal margin separator dan simpan pada variabel mm_separator.
Gunakan plot yang ditampilkan untuk menemukan nilai kadar gula dari titik-titik data ekstrem yang relevan pada setiap kelas.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

#The maximal margin separator is at the midpoint of the two extreme points in each cluster.
mm_separator <- (___ + ___)/2

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: Kandungan gula pada minuman ringan Exercise 2: Memvisualisasikan himpunan data kadar gula Exercise 3: Mengidentifikasi batas keputusan Exercise 4: Temukan pemisah margin maksimal

Latihan Saat Ini

Exercise 5: Visualisasikan pemisah margin maksimal Exercise 6: Membuat himpunan data yang terpisahkan secara linear Exercise 7: Bangun himpunan data 2D yang berdistribusi seragam.Exercise 8: Buat batas keputusan Exercise 9: Menambahkan margin pada himpunan data

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: 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