Wann sind Soft-Margin-Klassifikatoren nützlich?

In dieser Lektion haben wir ein Beispiel gesehen, in dem ein linearer SVM mit Soft Margin (niedrige Kosten, breite Margin) eine bessere Genauigkeit hatte als sein Hard-Margin-Gegenstück (hohe Kosten, schmale Margin). Welche der folgenden Formulierungen vervollständigt die Aussage am besten:

Lineare Soft-Margin-Klassifikatoren sind höchstwahrscheinlich nützlich, wenn:

Diese Übung ist Teil des Kurses

<Kurs>Support Vector Machines in R</Kurs>

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Verwandle Theorie mit einer unserer interaktiven Übungen in die Praxis

Diese Übung ist Teil des Kurses

<Kurs>Support Vector Machines in R</Kurs>

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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: Lineare Support-Vector-Machines Exercise 2: Trainings- und Testdatensätze erstellen Exercise 3: Einen linearen SVM-Klassifikator erstellen Exercise 4: Das Modell erkunden und Genauigkeit berechnen Exercise 5: Lineare SVMs visualisieren Exercise 6: Support-Vektoren mit ggplot visualisieren Exercise 7: Entscheidungs- und Randgrenzen mit `ggplot2` visualisieren Exercise 8: Entscheidungs- und Margin-Grenzen mit `plot()` visualisieren Exercise 9: Lineare SVMs tunen Exercise 10: Tuning eines linearen SVM Exercise 11: Entscheidungsgrenzen und Margen visualisieren Exercise 12: Wann sind Soft-Margin-Klassifikatoren nützlich?

Aktuelle Übung

Exercise 13: Multiklassenprobleme Exercise 14: Ein Multiclass-Klassifikationsproblem Exercise 15: Iris reloaded – eine robustere Genauigkeit.

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