Membuat himpunan data pelatihan dan pengujian

Membagi himpunan data menjadi himpunan pelatihan dan pengujian adalah langkah penting dalam membangun dan menguji model klasifikasi. Himpunan pelatihan digunakan untuk membangun model, sedangkan himpunan pengujian untuk mengevaluasi akurasi prediktifnya.

Dalam latihan ini, Anda akan membagi himpunan data yang Anda buat pada bab sebelumnya menjadi himpunan pelatihan dan pengujian. Himpunan data telah dimuat dalam data frame df dan seed sudah ditetapkan untuk memastikan reproduktabilitas. Ingat bahwa pada video sebelumnya, kita menetapkan batas atas untuk panjang himpunan pelatihan dengan beberapa fungsi yang praktis — sekarang giliran Anda untuk mengimplementasikannya!

Latihan ini adalah bagian dari kursus

Support Vector Machines di R

Petunjuk latihan

Tentukan batas atas untuk jumlah baris yang akan masuk ke himpunan pelatihan dan simpan dalam sample_size.
Buat vektor train yang menyimpan penugasan acak untuk himpunan pelatihan menurut proporsi 80/20.
Masukkan baris-baris dalam vektor train ke data frame trainset dan sisanya ke data frame testset.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Set the upper bound for the length of the training set
sample_size <- ___(___ * nrow(df))

# Assign rows to training set randomly
train <- ___(seq_len(nrow(df)), size = ___)

# Yield training and test sets
trainset <- df[___, ]
testset <- df[-___, ]

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

Latihan Saat Ini

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 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