`tune.svm()` gebruiken

In deze oefening ga je praktisch aan de slag met de functie tune.svm(). Je gebruikt die om de optimale waarden te vinden voor de parameters cost, gamma en coef0 voor een SVM-model op basis van de radiaal scheidbare gegevensset die je eerder in dit hoofdstuk hebt gemaakt. De trainingsgegevens staan in de dataframe trainset, de testgegevens in testset, en de bibliotheek e1071 is al voor je geladen. Onthoud dat de klassevariabele y in de derde kolom van trainset en testset staat.

Herinner je ook dat Kailash in de video cost=10^(1:3) gebruikte om een bereik voor de cost-parameter te krijgen van 10=10^1 tot 1000=10^3 in stappen van 10.

Deze oefening maakt deel uit van de cursus

Support Vector Machines in R

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Oefeninstructies

Stel de parameterzoekbereiken als volgt in:
- cost - van 0,1 (10^(-1)) tot 100 (10^2) in stappen van 10.
- gamma en coef0 - een van de volgende waarden: 0,1, 1 en 10.

Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

#tune model
tune_out <- 
    tune.svm(x = trainset[, -3], y = trainset[, 3], 
             type = "C-classification", 
             kernel = "polynomial", degree = 2, cost = 10^(___:___), 
             gamma = c(___, ___, ___), coef0 = c(0.1, 1, 10))

#list optimal values
tune_out$best.parameters$___
tune_out$best.parameters$___
tune_out$best.parameters$___

Code bewerken en uitvoeren

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: 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: Een radiaal scheidbare gegevensset genereren Exercise 2: Een 2D radiaal scheidbare gegevensset genereren Exercise 3: De gegevensset visualiseren Exercise 4: Lineaire SVM's op radiaal scheidbare data Exercise 5: Lineaire SVM voor een radiaal scheidbare gegevensset Exercise 6: Gemiddelde nauwkeurigheid voor lineaire SVM Exercise 7: De kerneltruc Exercise 8: Getransformeerde radiaal-separeerbare data visualiseren Exercise 9: SVM met polynomiale kernel Exercise 10: SVM's afstemmen Exercise 11: `tune.svm()` gebruiken

Huidige oefening

Exercise 12: Het getunede model bouwen en visualiseren

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