Een multiclass-classificatieprobleem

In deze oefening ga je met de functie svm() uit de e1071-bibliotheek een lineaire multiclass SVM-classifier bouwen voor een gegevensset die perfect lineair scheidbaar is. Bereken de nauwkeurigheid op de training- en testset, en visualiseer het model met de trainingsdata. De trainings- en testgegevenssets staan in de dataframes trainset en testset. Gebruik de standaardinstelling voor de cost-parameter.

Deze oefening maakt deel uit van de cursus

Support Vector Machines in R

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Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

#load library and build svm model
library(___)
svm_model<- 
    svm(y ~ ., data = ___, type = "C-classification", 
        kernel = ___, scale = FALSE)

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Deze oefening maakt deel uit van de cursus

Support Vector Machines in R

SkillTag.level.intermediateSkillTag.label

4.9+

Begin de cursus 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: Lineaire Support Vector Machines Exercise 2: Trainings- en testgegevenssets maken Exercise 3: Een lineaire SVM-classifier bouwen Exercise 4: Het model verkennen en de nauwkeurigheid berekenen Exercise 5: Lineaire SVM's visualiseren Exercise 6: Support vectors visualiseren met ggplot Exercise 7: Beslissings- en margegrenzen visualiseren met `ggplot2`Exercise 8: Beslissings- en margeranden visualiseren met `plot()`Exercise 9: Lineaire SVM's afstemmen Exercise 10: Afstemmen van een lineaire SVM Exercise 11: Beslisgrenzen en marges visualiseren Exercise 12: Wanneer zijn soft-margin-classifiers nuttig?Exercise 13: Multiklasse-problemen Exercise 14: Een multiclass-classificatieprobleem

Huidige oefening

Exercise 15: Iris redux - een robuustere nauwkeurigheid.

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