Ottimizzare un SVM lineare

In questo esercizio studierai l’effetto della variazione del parametro cost sul numero di support vector per gli SVM lineari. Per farlo, costruirai due SVM: uno con cost = 1 e l’altro con cost = 100, e troverai il numero di support vector. Un insieme di dati per il training del modello è disponibile nel dataframe trainset.

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Support Vector Machines in R

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#build svm model, cost = 1
svm_model_1 <- svm(y ~ .,
                   data = ___,
                   type = "C-classification",
                   cost = ___,
                   kernel = "___",
                   scale = FALSE)

#print model details
___

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Questo esercizio fa parte del corso

Support Vector Machines in R

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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: Support Vector Machines lineari Exercise 2: Creare insieme di addestramento e di test Exercise 3: Creare un classificatore SVM lineare Exercise 4: Esplorare il modello e calcolare l'accuratezza Exercise 5: Visualizzare le SVM lineari Exercise 6: Visualizzare i support vector con ggplot Exercise 7: Visualizzare frontiera di decisione e margini con `ggplot2`Exercise 8: Visualizzare confini di decisione e margine usando `plot()`Exercise 9: Ottimizzare gli SVM lineari Exercise 10: Ottimizzare un SVM lineare

Esercizio in corso

Exercise 11: Visualizzare i confini di decisione e i margini Exercise 12: Quando sono utili i classificatori a margine morbido?Exercise 13: Problemi multiclasse Exercise 14: Un problema di classificazione multiclasse Exercise 15: Iris redux - un'accuratezza più robusta.

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