Visualizzare l'insieme di dati

In questo esercizio userai ggplot() per visualizzare l'insieme di dati complesso che hai creato negli esercizi precedenti. L'insieme di dati è disponibile nel dataframe df. Non è richiesto visualizzare il confine di decisione.

Qui userai coord_fixed() per dare agli assi x e y la stessa rappresentazione fisica nel grafico, facendo apparire i cerchi come cerchi invece che come ellissi.

Questo esercizio fa parte del corso

Support Vector Machines in R

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Istruzioni dell'esercizio

Carica la libreria di grafici richiesta.
Imposta gli argomenti del parametro aesthetics.
Imposta la funzione geom_ appropriata per uno scatter plot.
Specifica coordinate uguali aggiungendo coord_fixed() senza argomenti.

Esercizio pratico interattivo

Prova a risolvere questo esercizio completando il codice di esempio.

# Load ggplot2
library(___)

# Plot x2 vs. x1, colored by y
scatter_plot<- ggplot(data = df, aes(x = ___, y = ___, color = ___)) + 
    # Add a point layer
    geom___() + 
    scale_color_manual(values = c("red", "blue")) +
    # Specify equal coordinates
    coord_fixed()
 
scatter_plot

Modifica ed esegui il codice

Questo esercizio fa parte del corso

Support Vector Machines in R

IntermediárioNível de habilidade

4.9+

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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: 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: 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: Generare un insieme di dati complesso Exercise 2: Generare un insieme di dati complesso - parte 1 Exercise 3: Generare un insieme di dati complesso - parte 2 Exercise 4: Visualizzare l'insieme di dati

Esercizio in corso

Exercise 5: Motivare il kernel RBF Exercise 6: SVM lineare per insieme di dati complesso Exercise 7: SVM quadratica per insieme di dati complesso Exercise 8: Il kernel RBF Exercise 9: SVM polinomiale su un insieme di dati complesso Exercise 10: SVM con kernel RBF su un insieme di dati complesso Exercise 11: Messa a punto di un SVM con kernel RBF