Visualisierung eines Datensatzes zum Zuckergehalt

In dieser Übung erstellst du ein eindimensionales Streudiagramm mit 25 Messwerten zum Zuckergehalt von Softdrinks. Ziel ist es, deutliche Cluster im Datensatz zu visualisieren – als erster Schritt, um mögliche Entscheidungsgrenzen zu identifizieren.

Der Datensatz mit 25 Zuckergehaltsmessungen befindet sich in der Spalte sugar_content des Dataframes df, das bereits für dich geladen wurde.

Diese Übung ist Teil des Kurses

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

Übungsanweisungen

Lade das Paket ggplot2.
Liste die Variablen im Dataframe df auf.
Vervollständige den Code für das Streudiagramm. Verwende den Datensatz df, um die Zuckergehalte entlang der x‑Achse (bei y gleich null) zu plotten.
Schreibe ggplot()-Code, um den Zuckergehalt in df als Streudiagramm darzustellen. Erkennst du zwei klar unterscheidbare Cluster, die Proben mit hohem bzw. niedrigem Zuckergehalt entsprechen?

Interaktive praktische Übung

Versuche dich an dieser Übung, indem du diesen Beispielcode vervollständigst.

# Load ggplot2
___

# Print variable names
___

# Plot sugar content along the x-axis
plot_df <- ggplot(data = __, aes(x = ___, y = ___)) + 
    geom_point() + 
    geom_text(aes(label = sugar_content), size = 2.5, vjust = 2, hjust = 0.5)

# Display plot
plot_df

Code bearbeiten und ausführen

Diese Übung ist Teil des Kurses

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

Mittlere SchwierigkeitSchwierigkeitsgrad

4.8+

Kurs kostenlos starten

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: Zuckergehalt von Erfrischungsgetränken Exercise 2: Visualisierung eines Datensatzes zum Zuckergehalt

Aktuelle Übung

Exercise 3: Entscheidungsgrenzen erkennen Exercise 4: Finde den Maximalmargen-Separator Exercise 5: Den Separator mit maximalem Rand visualisieren Exercise 6: Einen linear trennbaren Datensatz erzeugen Exercise 7: Erzeuge einen zweidimensional gleichverteilten Datensatz.Exercise 8: Erzeuge eine Entscheidungsgrenze Exercise 9: Füge dem Datensatz einen Margin hinzu

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: 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